The Inter-cloud meta-scheduling

Inter-cloud is a recently emerging approach that expands cloud elasticity. By facilitating an adaptable setting, it purposes at the realization of a scalable resource provisioning that enables a diversity of cloud user requirements to be handled efficiently. This study’s contribution is in the inter-cloud performance optimization of job executions using metascheduling concepts. This includes the development of the inter-cloud meta-scheduling (ICMS) framework, the ICMS optimal schemes and the SimIC toolkit. The ICMS model is an architectural strategy for managing and scheduling user services in virtualized dynamically inter-linked clouds. This is achieved by the development of a model that includes a set of algorithms, namely the Service-Request, Service-Distribution, Service-Availability and Service-Allocation algorithms. These along with resource management optimal schemes offer the novel functionalities of the ICMS where the message exchanging implements the job distributions method, the VM deployment offers the VM management features and the local resource management system details the management of the local cloud schedulers. The generated system offers great flexibility by facilitating a lightweight resource management methodology while at the same time handling the heterogeneity of different clouds through advanced service level agreement coordination. Experimental results are productive as the proposed ICMS model achieves enhancement of the performance of service distribution for a variety of criteria such as service execution times, makespan, turnaround times, utilization levels and energy consumption rates for various inter-cloud entities, e.g. users, hosts and VMs. For example, ICMS optimizes the performance of a non-meta-brokering inter-cloud by 3%, while ICMS with full optimal schemes achieves 9% optimization for the same configurations. The whole experimental platform is implemented into the inter-cloud Simulation toolkit (SimIC) developed by the author, which is a discrete event simulation framework

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

A strategic analysis of Greek tourism: competitive position, issues and lessons

All Mediterranean countries adopted and implemented more or less the same model of tourism development based on 4Ss (Sun, Sea,Sand and Sex). The purpose of this paper is to analyse the experience of developing leisure tourism in Greece in order to draw some useful lessons for other tourism destinations. To address the study’s aim this paper adopts the approach of strategic analysis. This approach illustrates the importance of tourism as an economic activity and analyses the current situation and structural problems of Greece as a destination. Although Greece has a wonderful range of natural, cultural and heritage resources, the lack of differentiation of the tourism offering as well as competitive disadvantages in the fields of governance, planning and marketing caused an overdependence on tour operatorsfor the promotion and distribution of its tourism products. The same factors compromised the quality of tourism services having involved a vicious circle. This situation has a negative impact on the sustainability and competitiveness of the destination and tourism industry. Therefore, the paper assesses the Greek experience with the aim to identify the crucial issues and challenges. This evaluation permits to take some lessons from the Greek experience, beneficial to other destinations willing to develop tourism, and to formulate some recommendations

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

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

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

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

Detecting performance degradation in cloud systems using LSTM autoencoders

Cloud computing technology is on the rise as it provides an easy to scale environment for Internet users in terms of computational resources. At the same time, cloud providers manage this demand for computational power by offering a pay per use model for virtualized resources. Yet, it is a challenging issue to administer the variety of different cloud applications and ensure high performance by identifying failures and errors on runtime. Distributed applications are error-prone, and creating a platform to support minimum hardware and software failures is a key challenge. In this work, we focus on anomaly detection of data storage systems, and we propose a solution for detecting performance degradation of cloud deployed systems in real time. We use Long Short-term Memory (LSTM) Autoencoders for learning the normal representations and reconstruct the input sequences. Then, we used the reconstructed errors of the LSTM Autoencoders on unseen time series data to detect abnormal behaviours. We used state-of-the-art benchmarks such as TPCx-IoT and YCSB to evaluate the performance of HBase and MongoDB systems. Our experimental analysis shows the ability of the proposed approach to detect abnormal behaviours in cloud systems