Cloud Domain Research Articles

Categorization of Intercloud users and auto-scaling of resources

Optimal allocation of resources in Intercloud computing is NP-complete program. Constraints are many and configuration of each cloud varies from each other. The mapping of the tasks to available virtual machines is challenging. In real life scenarios customer requirements may change. The complexity of the problem increases as requirement changes in terms of capacity, speed and time. To tide overfrequent changes in customer requirement and optimum utilization of available resources, a heuristic algorithm is proposed which will fit to the specification. The proposed algorithm is primarily divided into three phases, namely categorization of users, genetic algorithm-based resource allocation and earliest deadline first scheduling. The objective is to map the tasks to be executed to available VMs of the multi-cloud federation in order to have minimum makespan time and maximum customer satisfaction. After pr simulation on synthetic data, compared the simulation results with the existing scheduling algorithm. Results of the simulation confirm that the proposed categorization of the user in cloud domain can be beneficial in many folds and can address the existing challenges as per concerned metrics.

Effectiveness of Segment Routing Technology in Reducing the Bandwidth and Cloud Resources Provisioning Times in Network Function Virtualization Architectures

Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs.

Intelligent decision making service framework based on analytic hierarchy process in cloud environment

Outsourcing corporate procedures to a cloud domain is seen among the arrangements helping small and medium-sized corporate to rise in business sector and improve their additional worth. When deciding to outsource some portion of its corporate procedure to the cloud, small and medium-sized corporate ought to examine a few variables that define its particular connection. Given different variables on which depends the corporate process subcontracting to the cloud environment, a venture clearly needs a basic leadership technique. We define an endeavour setting that contains distinctive elements that help in outsourcing choice to the cloud. The manuscript demonstrates how corporate incentive model of venture additionally improves the outsourcing basic leadership by considering the corporate arrangements and vision of the undertaking. Finally, it proposes an outsourcing choice strategy that depends on expository order procedure to choose whether or not to outsource a corporate procedure to the cloud and through which administration sort.

Intelligent decision making service framework based on analytic hierarchy process in cloud environment

Outsourcing corporate procedures to a cloud domain is seen among the arrangements helping small and medium-sized corporate to rise in business sector and improve their additional worth. When deciding to outsource some portion of its corporate procedure to the cloud, small and medium-sized corporate ought to examine a few variables that define its particular connection. Given different variables on which depends the corporate process subcontracting to the cloud environment, a venture clearly needs a basic leadership technique. We define an endeavour setting that contains distinctive elements that help in outsourcing choice to the cloud. The manuscript demonstrates how corporate incentive model of venture additionally improves the outsourcing basic leadership by considering the corporate arrangements and vision of the undertaking. Finally, it proposes an outsourcing choice strategy that depends on expository order procedure to choose whether or not to outsource a corporate procedure to the cloud and through which administration sort.

Analysis of joint parallelism in wireless and cloud domains on mobile edge computing over 5G systems

The realization of mobile edge computing (MEC) over emerging fifth (5G) generation of wireless systems arises as a driving-force in the future of cloud computing. In order to cope with the volume, variety, and velocity of the IoT traffic while making optimal use of the network infrastructure, a synergistic exploitation of MEC and 5G should be put forward to support advanced resource management applications. In this paper, we propose the use of joint parallelism between wireless and cloud domains to efficiently respond to mobile data deluge. We review the literature, discuss the enabling network architecture, potentials, challenges, and open issues related to the realization of such level of parallelism. We present and evaluate two design examples — parallel computation offload method (PCOM) and parallel transmission and storage method (PTSM)—which outline the benefits of parallelism for computation-hungry and storage-hungry applications, respectively. Results of our optimization formulation show that PCOM and PTSM are able to make an efficient use of the network resources and support a heavy instantaneous workload by means of the parallelism.

An efficient cloud based face recognition system for e-health secured login using steerable pyramid transform and local directional pattern

This paper proposes a face recognition system based on steerable pyramid transform (SPT) and local directional pattern (LDP) for e-health secured login in cloud domain. In an e-health login, patients periodically forget their self-made login username and password. This face recognition system can replace the traditional login system. In the proposed system, SPT can decompose a face image into several sub-bands of different scales and orientations, and LDP can encode the sub-bands in binary texture pattern. The LDP binary pattern was obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Therefore, SPT–LDP design represents a face image in a robust way which includes multiple information sources from different scales and orientations. The proposed system is tested on standard benchmark facial recognition technology (FERET) and extended Yale-B databases. Further, the efficiency of the proposed system is proved by comparing recognition rates with other existing descriptor based methods. From the experimental results it is observed that the proposed system achieves 99.79% recognition in fb set, 90.72% in fc set, 84.97% in dup I set, and 87.69% in dup II set for FERET database and 100% in sub2 set, 100% in sub3 set, 96% in sub4 set and 95% in sub5 set for extended Yale-B database which is better than the existing methods.

Challenges of Cloud Computing Adoption From the TOE Framework Perspective

Cloud computing can be classified as a third-generation computing platform which refers to on-demand delivery of computing infrastructure and services via a network, usually the Internet. Cloud computing promises to provide several advantages to its adopters such as: cost advantage, availability, scalability, flexibility, reduced time to market and dynamic access to computational resources. Notwithstanding the numerous advantages of cloud computing, its implementation and adoption in developing countries is still limited and surrounded by variety of issues. Hence, the main objective of this article is to identify the main challenges facing the utilization of these services in developing countries, particularly Jordan. To achieve the above-mentioned objective, six in-depth interviews with ICT officials and experts in the domain of cloud computing were used as the main data collection method. The challenges of cloud computing adoption emerged in this study are classified into technological, organizational and environmental factors.

Monitoring Elastically Adaptive Multi-Cloud Services

Automatic resource provisioning is a challenging and complex task. It requires for applications, services and underlying platforms to be continuously monitored at multiple levels and time intervals. The complex nature of this task lays in the ability of the monitoring system to automatically detect runtime configurations in a cloud service due to elasticity action enforcement. Moreover, with the adoption of open cloud standards and library stacks, cloud consumers are now able to migrate their applications or even distribute them across multiple cloud domains. However, current cloud monitoring tools are either bounded to specific cloud platforms or limit their portability to provide elasticity support. In this article, we describe the challenges when monitoring elastically adaptive multi-cloud services. We then introduce a novel automated, modular, multi-layer and portable cloud monitoring framework. Experiments on multiple clouds and real-life applications show that our framework is capable of automatically adapting when elasticity actions are enforced to either the cloud service or to the monitoring topology. Furthermore, it is recoverable from faults introduced in the monitoring configuration with proven scalability and low runtime footprint. Most importantly, our framework is able to reduce network traffic by 41 percent, and consequently the monitoring cost, which is both billable and noticeable in large-scale multi-cloud services.

Optimal VNFs Placement in CDN Slicing Over Multi-Cloud Environment

This paper introduces a content delivery network as a service (CDNaaS) platform that allows dynamic deployment and life-cycle management of virtual content delivery network (CDN) slices running across multiple administrative cloud domains. The CDN slice consists of four virtual network function (VNF) types, namely virtual transcoders, virtual streamers, virtual caches, and a CDN-slice-specific Coordinator for the management of the slice resources across the involved cloud domains. To create an efficient CDN slice, the optimal placement of its composing VNFs using adequate amount of virtual resources for each VNF is of vital importance. In this vein, this paper devises mechanisms for allocating an appropriate set of VNFs for each CDN slice to meet its performance requirements and minimize as much as possible the incurred cost in terms of allocated virtual resources. A mathematical model is developed to evaluate the performance of the proposed mechanisms. We first formulate the VNF placement problem as two Linear Integer problem models, aiming at minimizing the cost and maximizing the quality of experience (QoE) of the virtual streaming service. By applying the bargaining game theory, we ensure an optimal tradeoff solution between the cost efficiency and QoE. Extensive simulations are conducted to evaluate the effectiveness of the proposed models in achieving their design objectives and encouraging results are obtained.

SSLB: Self-Similarity-Based Load Balancing for Large-Scale Fog Computing

As a novelty approach to achieve Internet of things and an important supplement of cloud, fog computing has been widely studied in recent years. The research in this domain is still in infancy, so an efficient resource management is seriously required. Existing solutions are mostly ported from cloud domain straightforward, which performed well in many cases, but cannot keep excellent when the fog scale increased. In this paper, we examine the runtime characteristics of fog infrastructure and propose SSLB, a self-similarity-based load balancing mechanism for large-scale fog computing. As far as we know, this is the first work try to address the load balancing challenges caused by fog’s ‘large-scale’ characteristic. Furthermore, we propose an adaptive threshold policy and corresponding scheduling algorithm, which successfully guarantees the efficiency of SSLB. Experimental results show that SSLB outperforms existing schemes in fog scenario. Specifically, the resources utilization of SSLB is 1.7 $$\times $$ and 1.2 $$\times $$ of traditional centralized and decentralized schemes under 1000 nodes.

Software-Defined “Hardware” Infrastructures: A Survey on Enabling Technologies and Open Research Directions

This paper provides an overview of software-defined “hardware” infrastructures (SDHI). SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource disaggregation breaks today's physical server-oriented model where the use of a physical resource (e.g., processor or memory) is constrained to a physical server's chassis. SDHI extends the definition of of software-defined infrastructures (SDI) and brings greater modularity, flexibility, and extensibility to cloud infrastructures, thus allowing cloud operators to employ resources more efficiently and allowing applications not to be bounded by the physical infrastructure's layout. This paper aims to be an initial introduction to SDHI and its associated technological advancements. This paper starts with an overview of the cloud domain and puts into perspective some of the most prominent efforts in the area. Then, it presents a set of differentiating use-cases that SDHI enables. Next, we state the fundamentals behind SDI and SDHI, and elaborate why SDHI is of great interest today. Moreover, it provides an overview of the functional architecture of a cloud built on SDHI, exploring how the impact of this transformation goes far beyond the cloud infrastructure level in its impact on platforms, execution environments, and applications. Finally, an in-depth assessment is made of the technologies behind SDHI, the impact of these technologies, and the associated challenges and potential future directions of SDHI.

Making the most of cloud storage - a toolkit for exploitation by WLCG experiments

Understanding how cloud storage can be effectively used, either standalone or in support of its associated compute, is now an important consideration for WLCG.We report on a suite of extensions to familiar tools targeted at enabling the integration of cloud object stores into traditional grid infrastructures and workflows. Notable updates include support for a number of object store flavours in FTS3, Davix and gfal2, including mitigations for lack of vector reads; the extension of Dynafed to operate as a bridge between grid and cloud domains; protocol translation in FTS3; the implementation of extensions to DPM (also implemented by the dCache project) to allow 3rd party transfers over HTTP.The result is a toolkit which facilitates data movement and access between grid and cloud infrastructures, broadening the range of workflows suitable for cloud. We report on deployment scenarios and prototype experience, explaining how, for example, an Amazon S3 or Azure allocation can be exploited by grid workflows.

A Novel Algorithm for Load Balancing in Mobile Cloud Networks: Multi-objective Optimization Approach

Cloud computing is a type of computing, because they share computing resources for better throughput. In cloud, virtualization technique is used to maximize the power of cloud computing. Mobile cloud computing architecture is based on geographical distributed cloud services. The resource allocation for the request is an important criterion for better performance of the computations. We need to efficiently handle these multiple cloud domains for better performance, quality of service and cost efficient job completion. In this paper, a novel method for efficiently allocating requirements to the available resources is proposed. Based on the prediction method a single cloud domain is selected from the multi cloud domain system. Then prioritize the nodes, based on the results obtained from the prediction approach. The priority ranks are arranged in descending order. The best resource allocation in the selected node (cloud domain) is determined by using genetic algorithm. Multi objective optimization method gives the constrained values for repetition of genetic approach. Using this priority based selection of nodes; unimportant node selection time is reduced, irrelevant node usage eliminated and the network congestion is reduced. Simulation results reveals how efficiently place the request to the appropriate resources on the nearest cloud domain and find out the best VM (virtual machine) for the individual request. Usage of rejected requests handling queues effectively handles the rejected requests. So the request rejection problem is eliminated.

An integrated information lifecycle management framework for exploiting social network data to identify dynamic large crowd concentration events in smart cities applications

With the current availability of an extreme diversity of data sources and services, emerging from the Internet of Things and Cloud domains, the challenge is shifted towards identifying intelligent, abstracted and adaptive ways of correlating and combining the various levels of information. The purpose of this work is to demonstrate such a combination, on one hand at the service level, through integrating smart cities platforms for user level data, and on the other hand at Complex Event Processing, Storage and Analytics capabilities together with Twitter data. The final goal is to identify events of interest to the user such as Large Crowd Concentration (LCC) in a given area, in order to enrich application level information with related event identification that can enable more sophisticated actions on behalf of that user. The identification is based on observation of Twitter activity peaks compared to historical data on a dynamic time and location of interest. The approach is validated through a two-month experiment in the city of Madrid, identifying LCCs in sporting events around two sports venues and analyzing various approaches with relation to the needed thresholds definition.

A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off Debates

Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and environmental conditions, with possible overlaps between different research works. Therefore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and deployment environment of Cloud applications, and identified 18 environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.

Attribute‐based access control management for multicloud collaboration

SummarySecurity of applications has been identified as one of the major concerns in today's multicloud collaborative environment. These applications are often bounded by the constraints of the disparate cloud domains they are deployed in. A fine‐grained access control mechanism such as attribute‐based access control (ABAC) is considered to be an appropriate choice for authorization management in this context. However, identifying a suitable set of ABAC rules, often called rule mining, is a critical step in building ABAC‐based systems. We propose 2 approaches for intercloud rule formation in ABAC. In the first approach, we consider cross domain rule mining as the problem of forming a minimal set of positive authorizations only. The second approach shows the advantage of developing deny rules along with positive authorizations in reducing the total number of rules, and hence, the response time for evaluating access requests. The problem is proved to be NP‐hard. Heuristic solutions are proposed and evaluated on benchmark datasets showing encouraging results.

Dynamic bidding in spot market for profit maximization in the public cloud

In public cloud domain, some cloud providers sell surplus resources in the form of spot instances to improve their profits. The average spot price is much cheaper than the on-demand options, which drives more and more cloud users to use spot instances to accelerate their services. However, the cloud user faces a challenge in determining the instance rental and management policy due to the unconventional pricing structure in the spot market and fluctuations in workload and spot price. In this work, we propose a dynamic bidding and resource management algorithm for the cloud user to cut down the instance rental bill in the spot market. The proposed algorithm operates in two timescales, i.e., it determines the bidding, instance rental, and job dispatching policy hourly and makes instances allocation decision in a finer granularity. The advantages of our approach are that it has a simple structure and needs no a priori statistical information of spot price and workload. We prove the optimality of the proposed algorithm and use extensive simulations to study its performance. It is shown that D-bid can save up to almost 70% of the rental cost compared to a baseline nonwork-conserving strategy.

HERMES: Federating Fog and Cloud Domains to Support Query Evaluations in Continuous Sensing Environments

As networked sensing devices continue to proliferate, the storage and processing capabilities of fog or edge devices have also increased substantially while offering low energy consumption and hardware costs. These complimentary technologies enable unique opportunities for performing decentralized analysis on the edges of the network while also leveraging the capabilities of public and private clouds for coordination and long-term storage. Our framework, HERMES, enables federated query evaluations that transition between cloud and fog nodes seamlessly. The system selectively samples from observational streams to reduce communication and memory consumption on fog nodes; our evaluation over a real-world observational dataset demonstrates an 89% reduction in dataset size while maintaining a mean absolute error of less than 0.25%.

Softwarization of Internet of Things Infrastructure for Secure and Smart Healthcare

We propose an agile softwarized infrastructure for flexible, cost effective, secure and privacy preserving deployment of Internet of Things (IoT) for smart healthcare applications and services. It integrates state-of-the-art networking and virtualization techniques across IoT, fog and cloud domains, employing Blockchain, Tor and message brokers to provide security and privacy for patients and healthcare providers. We propose a novel platform using Machine-to-Machine (M2M) messaging and rule-based beacons for seamless data management and discuss the role of data and decision fusion in the cloud and the fog, respectively, for smart healthcare applications and services.

A QoS Aware Cloud Service Composition Algorithm for Geo-Distributed Multi Cloud Domain

Publishing the cloud services on the internet has proven to be effective for cloud providers over the internet. To meet user requirements, services should be combined through service composition from multiple cloud domains. In this paper, we proposed an algorithm which composes best QoS aware services with minimal number of cloud combinations which is an NP-hard optimization problem. The proposed algorithm will select a cloud with more number of service files and then we applied PROMETHEE a multi criteria decision making method that selects the best service based on QoS criteria (i.e. Response time, Throughput, Availability, Successability, Price) from optimal cloud combinations. Our experimental results show that the algorithm provides better QoS services with minimal number of clouds by assessing previous benchmarks.