Distributed Cloud Applications Research Articles

Strega : An HTTP Server for FPGAs

The computer architecture landscape is being reshaped by the new opportunities, challenges, and constraints brought by the cloud. On the one hand, high-level applications profit from specialised hardware to boost their performance and reduce deployment costs. On the other hand, cloud providers maximise the CPU time allocated to client applications by offloading infrastructure tasks to hardware accelerators. While it is well understood how to do this for, e.g., network function virtualisation and protocols such as TCP/IP, support for higher networking layers is still largely missing, limiting the potential of accelerators. In this article, we present Strega , an open source 1 light-weight Hypertext Transfer Protocol (HTTP) server that enables crucial functionality such as FPGA-accelerated functions being called through a RESTful protocol (FPGA-as-a-Function). Our experimental analysis shows that a single Strega node sustains a throughput of 1.7 M HTTP requests per second with an end-to-end latency as low as 16, μs, outperforming nginx running on 32 vCPUs in both metrics, and can even be an alternative to the traditional OpenCL flow over the PCIe bus. Through this work, we pave the way for running microservices directly on FPGAs, bypassing CPU overhead and realising the full potential of FPGA acceleration in distributed cloud applications.

Building a Monitoring Framework for a Distributed Cloud Application Using Prometheus and Chef - An Overview

Abstract: A Distributed Cloud Application has multiple in-stances dedicated for specific use cases, which requires constant monitoring to avoid hassles in the clients’ experience. This in-cludes checking for services, checking for ports and the statuses of various components and component groups present and running in the application. The monitoring framework would alert the support team of any issues, thereby helping them resolve issues immediately. This paper provides a high level generalized overview on building this monitoring framework and automating its installation using Chef

Intelligent Secure Storage Mechanism for Big Data

The management of big data became more important due to the wide spread adoption of internet of things in various fields. The developments in technology, science, human habits, etc., generates massive amount of data, so it is increasingly important to store and protect these data from attacks. Big data analytics is now a hot topic. The data storage facility provided by the cloud computing enabled business organizations to overcome the burden of huge data storage and maintenance. Also, several distributed cloud applications supports them to analyze this data for taking appropriate decisions. The dynamic growth of data and data intensive applications demands an efficient intelligent storage mechanism for big data. The proposed system analyzes IP packets for vulnerabilities and classifies data nodes as reliable and unreliable nodes for the efficient data storage. The proposed Apriori algorithm based method automatically classifies the nodes for intelligent secure storage mechanism for the distributed big data storage.

An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

Method, formalization, and algorithms to split topology models for distributed cloud application deployments

For automating the deployment of applications in cloud environments, a variety of technologies have been developed in recent years. These technologies enable to specify the desired deployment in the form of deployment models that can be automatically processed by a provisioning engine. However, the deployment across several clouds increases the complexity of the provisioning. Using one deployment model with a single provisioning engine, which orchestrates the deployment across the clouds, forces the providers to expose low-level APIs to ensure the accessibility from outside. In this paper, we present an extended version of the split and match method to facilitate the division of deployment models to multiple models which can be deployed by each provider separately. The goal of this approach is to reduce the information and APIs which have to be exposed to the outside. We present a formalization and algorithms to automate the method. Moreover, we validate the practical feasibility by a prototype based on the TOSCA standard and the OpenTOSCA ecosystem.

Towards an understanding of cloud services under uncertainty: A possibilistic approach

With the development of Web technologies and the increasing use of the Internet, more and more web services are being deployed. This gave birth to what called cloud services, which are widely used for building distributed cloud applications. With cloud-based service delivery, it seems hard for users to find the right service for their needs. However, if a single cloud service cannot meet all user requirements, a cloud service composition is required. With the rise of Web services on the Internet, the quality of response has been considered as an important criterion to choose the most relevant answer. The performance of web services may vary depending on the dynamic Internet environment, which makes the quality of service uncertain. Then, the need to deal with uncertainty in the operation of cloud services. Over the past decade, several approaches to service composition have been proposed to cope with this challenge. In this article, we provide a flexible and efficient cloud services composition framework that can respond to user queries and improve cloud services composition results by exploiting data uncertainty. We then introduce an effective strategy that allows modeling, invocation, and composition of services by dealing with data uncertainty. The experimental evaluation of a real case study demonstrates the effectiveness of our proposed strategy.

CloudMF

While the number of cloud solutions is continuously increasing, the development and operation of large-scale and distributed cloud applications are still challenging. A major challenge is the lack of interoperability between the existing cloud solutions, which increases the complexity of maintaining and evolving complex applications potentially deployed across multiple cloud infrastructures and platforms. In this article, we show how the Cloud Modelling Framework leverages model-driven engineering and supports the DevOps ideas to tame this complexity by providing: (i) a domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for their continuous provisioning, deployment, and adaptation.

Asynchronous synthesis techniques for coordinating autonomic managers in the cloud

Cloud computing allows the delivery of on-demand computing resources over the Internet on a pay-for-use basis. From a technical point of view, cloud applications usually consist of several software components deployed on remote virtual machines. Managing such applications is a challenging problem because manual administration is no longer realistic for these complex distributed systems. Thus, autonomic computing is a promising solution for monitoring and updating these applications automatically. This is achieved through the automation of administration functions and the use of control loops called autonomic managers. An autonomic manager observes the environment, detects changes, and reconfigures dynamically the application. Multiple autonomic managers can be deployed in the same system and must make consistent decisions. Using them without coordination may lead to inconsistencies and error-prone situations. In this article, we first present a simple language for expressing coordination constraints given a set of autonomic managers. Second, given a coordination expression written with that language, we propose new synthesis techniques for automatically generating an asynchronous controller. These synthesis techniques work in two steps by successively generating a model of the controller and a Java object corresponding to this model. This Java code is finally used for deploying the generated controller. As far as evaluation is concerned, we validated our approach by using it for coordinating real-world cloud applications. • A new approach for coordinating multiple autonomic managers for distributed cloud applications. • New synthesis techniques for generating centralised controllers in asynchronous environments (FIFO-based communication). • A fully automated tool support that covers the whole development process from the requirements to the final deployment of our solution. • The evaluation of our approach on real-world multi-tier Web applications.

Efficient Modeling and Demand Allocation for Differentiated Cloud Virtual-Network as-a Service Offerings

Cloud clients (CCs) of current distributed cloud applications are still not assured of their service quality, in particular, in terms of the experienced latency. Unfortunately, this is mainly attributed to the unpredictability of the communication links among their hosting distributed data centers. To address this problem, this article introduces a novel virtual-network-as-a-service (VNaaS) model to host these applications. In contrast to existing randomly or statically provisioned inter-data centers bandwidth sharing models, the proposed model allows CCs to accurately express their varying network resources needs, demand constraints and tolerance to the cloud latency. In turn, the model maps these requirements to create inter-data centers virtual links hosting each multiple virtual pipes with differentiated service qualities to carry the CC's various traffic flows. To aid the CCs in optimally determining their VNaaS demands, given the budget constraints of their hosted applications, we also develop a novel demand selection scheme based on a two stage-budget allocation mechanism. In the first budgeting stage, the CC calculates an optimal effective service rate for each of its virtual link along with a corresponding link budget and price index. In the second stage, the virtual link budget is distributed to purchase bandwidth for the link's virtual pipes, each with a given service quality and pricing. We then extend the proposed model to allow the CC to enforce any required virtual links’ capacity constraints on the effective service rates resulting from the traffic matrix on the VNaaS. Finally, we develop corresponding differentiated VNaaS pricing and service monitoring mechanisms that can be employed by the cloud service provider (CSP) to regulate the offerings and demands of the distributed cloud services. Performance evaluation results demonstrate the significant improvement in the service quality, the higher utilization of the cloud resources and the increase in the CSP's net profit.

Reliable self-deployment of distributed cloud applications

Cloud applications consist of a set of interconnected software elements distributed over several virtual machines, themselves hosted on remote physical servers. Most existing solutions for deploying such applications require human intervention to configure parts of the system, do not conform to functional dependencies among elements that must be respected when starting them, and do not handle virtual machine failures that can occur when deploying an application. This paper presents a self-deployment protocol that was designed to automatically configure a set of software elements to be deployed on different virtual machines. This protocol works in a decentralized way, that is, there is no need for a centralized server. It also starts the software elements in a certain order, respecting important architectural invariants. This protocol supports virtual machine and network failures and always succeeds in deploying an application when faced with a finite number of failures. Designing such highly parallel management protocols is difficult; therefore, formal modeling techniques and verification tools were used for validation purposes. The protocol was implemented in Java and was used to deploy industrial applications. Copyright © 2016 John Wiley & Sons, Ltd.

Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications

Cloud Computing has evolved to become an enabler for delivering access to large scale distributed applications running on managed network-connected computing systems. This makes possible hosting Distributed Enterprise Information Systems (dEISs) in cloud environments, while enforcing strict performance and quality of service requirements, defined using Service Level Agreements (SLAs). SLAs define the performance boundaries of distributed applications, and are enforced by a cloud management system (CMS) dynamically allocating the available computing resources to the cloud services. We present two novel VM-scaling algorithms focused on dEIS systems, which optimally detect most appropriate scaling conditions using performance-models of distributed applications derived from constant-workload benchmarks, together with SLA-specified performance constraints. We simulate the VM-scaling algorithms in a cloud simulator and compare against trace-based performance models of dEISs. We compare a total of three SLA-based VM-scaling algorithms (one using prediction mechanisms) based on a real-world application scenario involving a large variable number of users. Our results show that it is beneficial to use autoregressive predictive SLA-driven scaling algorithms in cloud management systems for guaranteeing performance invariants of distributed cloud applications, as opposed to using only reactive SLA-based VM-scaling algorithms.

Modeling security requirements for cloud‐based system development

SummaryThe Cloud Computing paradigm provides a new model for the more flexible utilization of computing and storage services. However, such enhanced flexibility, which implies outsourcing the data and business applications to a third party, may introduce critical security issues. Therefore, there is a clear necessity of new security paradigms able to face all the problems introduced by the cloud approach. Although, in the last years, several solutions have been proposed, the implementation of secure cloud applications and services is still a complex and far from consolidated task. Starting from these considerations, this work fosters the development of a methodology that considers security concerns as an integral part of cloud‐based applications design and implementation. Accordingly, we present a set of stereotypes that defines a vocabulary for annotating Unified Modeling Language based models with information relevant for integrating the specification of security requirements into cloud architectures. This approach can be used to significantly improve productivity and overall success in the development of secure distributed cloud applications and systems. Copyright © 2014 John Wiley & Sons, Ltd.

Analytical modeling for what-if analysis in complex cloud computing applications

Modern cloud applications are complex distributed systems with tens or hundreds of interacting software components. An important management task in cloud computing platforms is to predict the impact of a certain workload or reconfiguration change on the performance of the application. Such predictions require the design of "what-if" models of the application that take as input hypothetical changes in the application's workload or environment and estimate its impact on performance. We present a workload-based what-if analysis system that uses commonly available monitoring information in large scale systems to enable the administrators to ask a variety of workload-based "what-if" queries about the system. We use a network of queues to analytically model the behavior of large distributed cloud applications. Our system automatically generates node-level queueing models and then uses model composition to build system-wide models. We employ a simple what-if query language and an intelligent query execution algorithm that employs on-the-fly model construction and a change propagation algorithm to efficiently answer queries on large scale systems. We have built a prototype and have used traces from two large production cloud bapplications from a financial institution as well as real-world synthetic applications to evaluate its what-if modeling framework. Our experimental evaluation validates the accuracy of our node-level resource usage, latency and workload models and then shows how our system enables what-if analysis in four different cloud applications.

State Monitoring in Cloud Datacenters

Monitoring global states of a distributed cloud application is a critical functionality for cloud datacenter management. State monitoring requires meeting two demanding objectives: high level of correctness, which ensures zero or low error rate, and high communication efficiency, which demands minimal communication cost in detecting state updates. Most existing work follows an instantaneous model which triggers state alerts whenever a constraint is violated. This model may cause frequent and unnecessary alerts due to momentary value bursts and outliers. Countermeasures of such alerts may further cause problematic operations. In this paper, we present a WIndow-based StatE monitoring (WISE) framework for efficiently managing cloud applications. Window-based state monitoring reports alerts only when state violation is continuous within a time window. We show that it is not only more resilient to value bursts and outliers, but also able to save considerable communication when implemented in a distributed manner based on four technical contributions. First, we present the architectural design and deployment options for window-based state monitoring with centralized parameter tuning. Second, we develop a new distributed parameter tuning scheme enabling WISE to scale to much more monitoring nodes as each node tunes its monitoring parameters reactively without global information. Third, we introduce two optimization techniques, including their design rationale, correctness and usage model, to further reduce the communication cost. Finally, we provide an in-depth empirical study of the scalability of WISE, and evaluate the improvement brought by the distributed tuning scheme and the two performance optimizations. Our results show that WISE reduces communication by 50-90 percent compared with instantaneous monitoring approaches, and the improved WISE gains a clear scalability advantage over its centralized version.