Cloud Infrastructure Providers Research Articles

Global Id Generation with Verification Based Medical Record Access

Global Id Generation with Verification Based Medical Record Access

Diseño de Interfaz de Usuario Web de un Editor Visual para el Modelamiento de Infraestructura en la Nube

Cloud computing has evolved the way IT technicians manage infrastructure resources to support software applications. Leasing equipment and services from cloud infrastructure providers, such as Amazon Web Services, Microsoft Azure, etc., has replaced the traditional strategy of locally installing expensive equipment. Nowadays, IT technicians model the infrastructure they need by writing scripts, then running these scripts in the provider web platform. However, writing scripts becomes a complex task that involves interacting with the Command Line Interface and knowing the commands each provider imposes. In this context, replacing textual with visual interaction becomes a need. The purpose is to fesign the user interface (UI) of a visual web editor to model the cloud infrastructure resources. The User-Centered Design (UCD) methodology was applied to design the UI. As part of DCU, a study of the UIs of diagramming online tools was conducted to identify UI design patterns; those that expert designers use when designing visual editors.The results show that a set of 11 UI patterns for designing visual editor UIs was defined. In addition, a pattern language was built considering the relationships between patterns. By using the pattern language, the visual editor UI design was composed. As conclusions, the pattern language provided a logical way to compose the visual editor UI. In this sense, the proposed UI together with the pattern language can become a reference point for designing UIs in this domain. In future work, the effectiveness of the UI in reducing the complexity of defining the cloud infrastructure will be evaluated with user tests.

Model-based cloud service deployment optimisation method for minimisation of application service operational cost

Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.

A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration

AbstractCloud computing is an established paradigm for end users to access resources. Cloud infrastructure providers seek to maximize accepted requests, meet Service Level Agreements (SLAs), and reduce operational costs by dynamically allocating Virtual Machines (VMs) to physical nodes. Many solutions have been presented to manage cloud infrastructure, however, these tend to be centralized and suffer in their ability to maintain Quality of Service (QOS) and support data centers with thousands of nodes. Decentralized approaches, with no central management, can manage large data centers. However, these tend to reduce the ability to obtain an optimal resource allocation across the data center. To address this, we propose a hybrid hierarchical decentralized architecture that achieves lower SLA violations and lowers network traffic. We used simulation to evaluate our proposal in practice with a variety of existing VM placement policies.

Security Monitoring for Multi-Cloud Native Network Service Based Functions

Abstract: Nowadays, enterprises are adopting a cloud- native to provide rapid change, large scale, and resilience in their application. The applications were built as independent services and packaged as self-contained, lightweight containers.And in order to provide leading applications, better performance, and avoid getting locked into a particular cloud provider's infrastructure. They choose to deploy a cloud-native application on a multi-cloud Infrastructure. While this native multi-cloud strategy has many benefits, it definitely adds more management complexity. We have proposed a framework by creating an abstraction layer that provides security and visibility across these multi-cloud services. We have visualized the metrics like request per second, status code, bandwidth, andlatencies of two sample API services (Users, and Products) which are publicly available and deployed on Google cloudfunction and on a CloudFlare.

Selective caching: a persistent memory approach for multi-dimensional index structures

After the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads.

Adaptive resource planning for cloud-based services using machine learning

The problem of using cloud computing resources for services is related to planning the amount of resources needed and their subsequent reservation. This problem occurs both on the side of the customer who tries to minimize the cost of the service and on the side of the cloud provider who wants to make the best use of existing infrastructure without introducing any modifications. In our article, we want to show how the problem of overestimating the utilization of resources for services which use cloud computing can be handled. Solving this problem will allow significant savings to be made by both the customer and the cloud infrastructure provider. The system we have developed demonstrates the considerable utility of machine learning methods when planning cloud resource reservation for network services. The models proposed, which use a multilayer perceptron, have yielded good results for both short- and long-term reservations.

Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers

AbstractOne of the foremost issues deliberated by the cloud infrastructure providers is to minimize the costs by reducing energy consumption. Though many heuristic algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and so on, have been existed for task scheduling or server consolidation to reduce energy consumption, more energy savings in data centers is required. In this paper, a combination of two distinctive heuristic algorithms, namely Particle Swarm Optimization (PSO) technique for proficient task scheduling and Ant Colony System (ACS) for effective server consolidation are proposed. In PSO, each particle in the swarm finds a task scheduling solution and updates the best solution. It is faster than many meta‐heuristic algorithms. In ACS, each ant in the ant colony tries to find the best destination PMs for migrating VMs from overloaded or underloaded PMs. The proposed PSO‐ACS algorithm is experimented with PlanetLab real workload traces available in the CloudSim toolkit. The algorithm provides reduction in energy consumption, number of VM migrations, and Energy SLA Violation (ESV) metrics while maintaining QoS. In the best‐case scenario, the proposed method reduces the ESV metric by 28%, 27%, and 18% in comparison with other algorithms like LR, ACS, and PSO‐LR.

High Availability Deployment of Virtual Network Function Forwarding Graph in Cloud Computing Environments

Network Function Virtualization (NFV) stands out quickly as a promising innovation in telecommunication networks. It leverages the concept of cloud technology and virtualization techniques. However, the continuity of cloud network services has become an essential availability requirement for NFV. Failure of Virtual Network Functions (VNFs) may cause critical quality assurance problems for such network services. VNF chaining and placement can be considered as the VNF Forwarding Graph (VNF-FG) mapped on a cloud provider infrastructure, also called NFV Infrastructure (NFVI). This mapping is addressed while neglecting the massive link utilization and bandwidth consumption that can be encountered during VNF recovery phase. In this paper, an Integer Linear Programming (ILP) approach is developed to model VNF-FG deployment problem while guaranteeing high availability against node/VM failures. Then, the Redundant VNF-FG Deployment (RVNF-FGD) heuristic algorithm is proposed that takes VNF migration into consideration. RVNF-FGD algorithm attempts to find a near-optimal solution that achieves a trade-off between availability and scalability with a reasonable convergence time. Thus, facilitating the practical deployment of the proposed approach. Simulation results confirm that RVNF-FGD algorithm is capable of simultaneously reducing link utilization and bandwidth consumption across the core layer of the cloud network. In addition, it reduces VNF-FG communication cost and overall energy consumption. The convergence time of RVNF-FGD algorithm is assessed by applying it to broader cloud network architectures. This assessment indicates the viability of the proposed approach in responding quickly to failures.

An Ultra-Fast and Parallelizable Algorithm for Finding k-Mismatch Shortest Unique Substrings.

This paper revisits the k-mismatch shortest unique substring finding problem and demonstrates that a technique recently presented in the context of solving the k-mismatch average common substring problem can be adapted and combined with parts of the existing solution, resulting in a new algorithm which has expected time complexity of O(n logk n), while maintaining a practical space complexity at O(kn), where n is the string length. When , which is the hard case, our new proposal significantly improves the any-case O(n2) time complexity of the prior best method for k-mismatch shortest unique substring finding. Experimental study shows that our new algorithm is practical to implement and demonstrates significant improvements in processing time compared to the prior best solution's implementation when k is small relative to n. For example, our method processes a 200 KB sample DNA sequence with k=1 in just 0.18 seconds compared to 174.37 seconds with the prior best solution. Further, it is observed that significant portions of the adapted technique can be executed in parallel, using two different simple concurrency models, resulting in further significant practical performance improvement. As an example, when using 8 cores, the parallel implementations both achieved processing times that are less than 1/4 of the serial implementation's time cost, when processing a 10 MB sample DNA sequence with k=2. In an age where instances with thousands of gigabytes of RAM are readily available for use through Cloud infrastructure providers, it is likely that the trade-off of additional memory usage for significantly improved processing times will be desirable and needed by many users. For example, the best prior solution may spend years to finish a DNA sample of 200MB for any , while this new proposal, using 24 cores, can finish processing a sample of this size with k=1 in 206.376 seconds with a peak memory usage of 46 GB, which is both easily available and affordable on Cloud. It is expected that this new efficient and practical algorithm for k-mismatch shortest unique substring finding will prove useful to those using the measure on long sequences in fields such as computational biology. We also give a theoretical bound that the k-mismatch shortest unique substring finding problem can be solved using O(n logk n) time and O(n) space, asymptotically much better than the one we implemented, serving as a new discovery of interest.

Live Cloud Ingest Using Open Alternatives RIST & SRT

As cloud production becomes an integral part of broadcasters’ live workflows, the corresponding cloud infrastructure becomes an integral part of the media transport network. Cloud infrastructure providers, often public in nature, are accessible either through the public internet or through leased lines. Regardless of public or private, experience shows that these connections are not up to the quality standards required for low latency live streaming media, making quality-enhancing technologies, such as retransmission or Automatic Repeat reQuest (ARQ), an imperative part of the network infrastructure to deliver on the requirements for broadcast quality transmission. Two solutions have emerged as front runners on the quest for the next de facto standard for broadcast retransmission—Reliable Internet Stream Transport (RIST) and Secure Reliable Transport (SRT). This report compares these two alternatives from a theoretical and performance perspective and compares them with a commercial reference alternative .

Low Power Consumption on Cloud Data Centers Using HSA Algorithm

The rapid development of knowledge and communication has created a new processing style called cloud computing. One of the key issues facing cloud infrastructure providers is minimizing costs and maximizing profitability. Power management in cloud centres is very important to achieve this. Energy consumption can be reduced by releasing inactive nodes or by reducing the migration of virtual machines. The second is one of the challenges of choosing the virtual machine deployment method to migrate to the right node. This article proposes an approach to reduce electricity consumption in cloud centres. This approach adapts Harmony's search algorithm to move virtual machines. Positioning is done by sorting nodes and virtual machines according to their priorities in descending order. Priority is calculated based on the workload. The proposed procedure is envisaged. The evaluation results show less virtual machine migration, greater efficiency and lower energy consumption.

Capacity Expansions with Bundled Supplies of Attributes: An Application to Server Procurement in Cloud Computing

Problem definition: The recent surge in demand for cloud services has posed a significant capacity-expansion problem for cloud infrastructure providers. Although the growth of demand for capacity attributes—for example, CPU and RAM—is disproportionate, these attributes are often provided in preconfigured packages (cluster-types), and the fixed ratio of attributes in a package does not match with the time-varying ratio of demand. We analyze a class of expansion policies to determine the timing and magnitude of expansions, using preconfigured cluster-types, and we examine the optimal configurations of the cluster-types. Academic/practical relevance: Cloud computing is a major technological advance that is influencing businesses significantly, giving rise to an emerging industry but also posing the above-noted capacity-expansion problem. To our knowledge, this is a new issue that has not been studied in the literature. Methodology: We consider growing demand for two attributes and analyze a class of policies that consist of capacity expansion cycles (CECs), whereby capacities are added through sequential or simultaneous replenishments of two configured cluster-types. Results: We first derive the optimal timing and magnitude of expansions for every CEC, and then we devise two algorithms, the dynamic-programming-based (DP) algorithm and the forward-looking (FL) heuristic, to determine the optimal cycle lengths. We also propose a cluster-selection heuristic for choosing the optimal configurations of the cluster-types. Managerial implications: The FL-heuristic is effective, easy to communicate, and can be used as an excellent starting point for the search of the DP-algorithm. Moreover, because there is a desire in practice to reduce the variety of cluster-types, we find conditions under which the employment of only two cluster-types is as efficient as the employment of many cluster-types. Finally, we provide useful guidelines for the optimal configurations of these two cluster-types.

Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.

A Container-Based Technique to Improve Virtual Machine Migration in Cloud Computing

Hypervisor-enabled virtualization is a promising technology to deploy applications and implement Virtual Machine (VM) migration on cloud data centers. However, one critical aspect in hypervisor-based solution is that to deploy applications each VM is installed with a dedicated guest OS along with the requirement of binaries and library files and substantially large VM's image size. Therefore, it is a significant concern for data center administrators because it causes performance overheads. To solve these issues, a new paradigm of virtualization technology that has attracted considerable attention is containerization. Although, progress has been made to address container-based virtualization, there has been less attention to explore the issue of performance evaluation of container and VM in a migration environment. The main contribution of this research study is to develop an experimental setup to implement and evaluate the performance of LXD/CR container-based migration technique and compared it with the pre-copy VM migration scheme. To conduct this performance evaluation, first we modified Linux kernel and implemented LXD which uses liblxc to launch container, then migration of the running container is facilitated using a checkpoint/restore mechanism of CRIU technique. To ensure the applicability and validity for real-time cloud infrastructure, we executed a wide variety of benchmarks such as web server, CPU-intensive, disk I/O and database. From the results obtained, it is found that compared to pre-copy VM migration scheme, LXD/CR container migration technique reduces the downtime, migration time, amount of data transferred and the number of pages transferred by 75.66%, 65.55%, 76.63%, and 76.78%, respectively. In addition, the migration overhead caused is reduced by 55.89% w.r.t. CPU utilization and 76.52% w.r.t. RAM utilization. This being an important contribution for migrating the running applications efficiently and consequently increase the business value of the cloud infrastructure providers.

Computing and Bandwidth Resource Allocation in Multi-Provider NFV Environment

We propose an algorithm for the cloud and bandwidth resource allocation in multi-provider NFV environments. The resources are allocated so as to take into account the different costs charged by the cloud infrastructure providers (InP). The effectiveness of the proposed algorithm is confirmed from the comparison with the results of the optimal problem. Its application in medium and large networks has shown that it can lead to cost saving as high as 65% with respect to algorithms that allocate resources without taking into account the cost differences charged by the InPs.

Realizing Software Reliability in the Face of Infrastructure Instability

Cloud computing has brought with it the utilization of off the shelf, commodity hardware that has higher failure rates than the systems that have been used in enterprises for the last several decades. Coupled with increasingly complex, highly-distributed, constantly-changing data center environments that can no longer be treated as deterministic systems, this forces us to change the way that we view the stability of that infrastructure. Our aim is to provide digital solutions that remain stable in the face of this infrastructure uncertainty and we achieve this by utilizing specific design patterns and operational practices. At the core of the new way of working is a philosophical view that change is the rule rather than the exception. US entertainment company Netflix has fully embraced this new mindset and this served them well during a major outage experienced by their cloud infrastructure provider, Amazon Web Services (AWS). In this piece, we study how Netflix was able to avoid any significant impact while many other well established, technically savvy AWS users were not.

Power efficient server consolidation for Cloud data center

Cloud computing has become an essential part of the global digital economy due to its extensibility, flexibility and reduced costs of operations. Nowadays, data centers (DCs) contain thousands of different machines running a huge number of diverse applications over an extended period. Resource management in Cloud is an open issue since an efficient resource allocation can reduce the infrastructure running cost. In this paper, we propose a snapshot-based solution for server consolidation problem from Cloud infrastructure provider (CIP) perspective. Our proposed mathematical formulation aims at reducing power cost by employing efficient server consolidation, and also considering the issues such as (i) mapping incoming and failing virtual machines (VMs), (ii) reducing a total number of VM migrations and (iii) consolidating running server workloads. We also compare the performance of our proposed model to the well-known Best Fit heuristics and its extension to include server consolidation via VM migration denoted as Best Fit with Consolidation (BFC). Our proposed mathematical formulation allows us to measure the solution quality in absolute terms, and it can also be applicable in practice. In our simulations, we show that relevant improvements (from 6% to 15%) over the widely adopted Best Fit algorithm achieved in a reasonable computing time.

Media Query Processing for the Internet-of-Things: Coupling of Device Energy Consumption and Cloud Infrastructure Billing

Audio/visual recognition and retrieval applications have recently garnered significant attention within Internet-of-Things-oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: 1) controlling the device energy consumption when using the service, and 2) reducing the billing cost incurred from the cloud infrastructure provider. In this paper, we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, the query volume constraint of each cluster of devices, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: 1) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service, and 2) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.

Unsupervised Learning of Dynamic Resource Provisioning Policies for Cloud-Hosted Multitier Web Applications

Dynamic resource provisioning for Web applications allows for low operational costs while meeting service-level objectives (SLOs). However, the complexity of multitier Web applications makes it difficult to automatically provision resources for each tier without human supervision. In this paper, we introduce unsupervised machine learning methods to dynamically provision multitier Web applications, while observing user-defined performance goals. The proposed technique operates in real time and uses learning techniques to identify workload patterns from access logs, reactively identifies bottlenecks for specific workload patterns, and dynamically builds resource allocation policies for each particular workload. We demonstrate the effectiveness of the proposed approach in several experiments using synthetic workloads on the Amazon Elastic Compute Cloud (EC2) and compare it with industry-standard rule-based autoscale strategies. Our results show that the proposed techniques would enable cloud infrastructure providers or application owners to build systems that automatically manage multitier Web applications, while meeting SLOs, without any prior knowledge of the applications' resource utilization or workload patterns.