Adaptive microservice scaling for elastic applications

Today, Internet users expect web applications to be fast, performant and always available. With the emergence of Internet of Things, data collection and the analysis of streams have become more and more challenging. Behind the scenes, application owners and cloud service providers work to meet these expectations, yet, the problem of how to most effectively and efficiently auto-scale a web application to optimise for performance whilst reducing costs and energy usage is still a challenge. In particular, this problem has new relevance due to the continued rise of Internet of Things and microservice based architectures. A key concern, that is often not addressed by current auto-scaling systems, is the decision on which microservice to scale in order to increase performance. Our aim is to design a prototype auto-scaling system for microservice based web applications which can learn from past service experience. The contributions of the work can be divided into two parts (a) developing a pipeline for microservice auto-scaling and (b) evaluating a hybrid sequence and supervised learning model for recommending scaling actions. The pipeline has proven to be an effective platform for exploring auto-scaling solutions, as we will demonstrate through the evaluation of our proposed hybrid model. The results of hybrid model show the merit of using a supervised model to identify which microservices should be scaled up more

A mobile agent strategy for grid interoperable virtual organisations

During the last few years much effort has been put into developing grid computing and proposing an open and interoperable framework for grid resources capable of defining a decentralized control setting. Such environments may define new rules and actions relating to internal Virtual Organisation (VO) members and therefore posing new challenges towards to an extended cooperation model of grids. More specifically, VO policies from the viewpoint of internal knowledge and capabilities may be expressed in the form of intelligent agents thus providing a more autonomous solution of inter-communicating members. In this paper we propose an interoperable mobility agent model that performs migration to any interacting VO member and by traveling within each domain allows the discovery of resources dynamically. The originality of our approach is the mobility mechanism based on traveling and migration which stores useful information during the route to each visited individual. The method is considered under the Foundation for Intelligent Physical Agents (FIPA) standard which provides an on demand resource provisioning model for autonomous mobile agents. Finally the decentralization of the proposed model is achieved by providing each member with a public profile of personal information which is available upon request from any interconnected member during the resource discovery process

Vertical and horizontal elasticity for dynamic virtual machine reconfiguration

Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud

Cloud scheduling optimization: a reactive model to enable dynamic deployment of virtual machines instantiations

This study proposes a model for supporting the decision making process of the cloud policy for the deployment of virtual machines in cloud environments. We explore two configurations, the static case in which virtual machines are generated according to the cloud orchestration, and the dynamic case in which virtual machines are reactively adapted according to the job submissions, using migration, for optimizing performance time metrics. We integrate both solutions in the same simulator for measuring the performance of various combinations of virtual machines, jobs and hosts in terms of the average execution and total simulation time. We conclude that the dynamic configuration is prosperus as it offers optimized job execution performance

From grids to clouds: a collective intelligence study for inter-cooperated infrastructures

Recently, more effort has been put into developing interoperable and distributed environments that offer users exceptional opportunities for utilizing resources over the internet. By utilising grids and clouds, resource consumers and providers, they gain significant benefits by either using or purchasing the computer processing capacities and the information provided by data centres. On the other hand, the collective intelligence paradigm is characterized as group based intelligence that emerges from the collaboration of many individuals, who in turn, define a coordinated knowledge model. It is envisaged that such a knowledge model could be of significant advantage if it is incorporated within the grid and cloud community. The dynamic load and access balancing of the grid and cloud data centres and the collective intelligence provides multiple opportunities, involving resource provisioning and development of scalable and heterogeneous applications. The contribution of this paper is that by utilizing grid and cloud resources, internal information stored within a public profile of each participant, resource providers as well as consumers, can lead to an effective mobilization of improved skills of members. We aim to unify the grid and cloud functionality as consumable computational power, for a) discussing the supreme advantages of such on-line resource utilization and provisioning models and b) analyzing the impact of the collective intelligence in the future trends of the aforementioned technologies

Meta-scheduling Issues in Interoperable HPCs, Grids and Clouds

Over the last years, interoperability among resources has been emerged as one of the most challenging research topics. However, the commonality of the complexity of the architectures (e.g., heterogeneity) and the targets that each computational paradigm including HPC, grids and clouds aims to achieve (e.g., flexibility) remain the same. This is to efficiently orchestrate resources in a distributed computing fashion by bridging the gap among local and remote participants. Initially, this is closely related with the scheduling concept which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings such as in grids and clouds. Within this context, meta-scheduling offers additional functionalities in the area of interoperable resource management, this is because of its great agility to handle sudden variations and dynamic situations in user demands. Accordingly, the case of inter-infrastructures, including InterCloud, entitle that the decentralised meta-scheduling scheme overcome issues like consolidated administration management, bottleneck and local information exposition. In this work, we detail the fundamental issues for developing an effective interoperable meta-scheduler for e-infrastructures in general and InterCloud in particular. Finally, we describe a simulation and experimental configuration based on real grid workload traces to demonstrate the interoperable setting as well as provide experimental results as part of a strategic plan for integrating future meta-schedulers

Lyashko-Looijenga morphisms and submaximal factorisations of a Coxeter element

When W is a finite reflection group, the noncrossing partition lattice NCP_W of type W is a rich combinatorial object, extending the notion of noncrossing partitions of an n-gon. A formula (for which the only known proofs are case-by-case) expresses the number of multichains of a given length in NCP_W as a generalised Fuss-Catalan number, depending on the invariant degrees of W. We describe how to understand some specifications of this formula in a case-free way, using an interpretation of the chains of NCP_W as fibers of a Lyashko-Looijenga covering (LL), constructed from the geometry of the discriminant hypersurface of W. We study algebraically the map LL, describing the factorisations of its discriminant and its Jacobian. As byproducts, we generalise a formula stated by K. Saito for real reflection groups, and we deduce new enumeration formulas for certain factorisations of a Coxeter element of W.Comment: 18 pages. Version 2 : corrected typos and improved presentation. Version 3 : corrected typos, added illustrated example. To appear in Journal of Algebraic Combinatoric