Cloud Nodes Research Articles

On the performance of the wide-area networks interconnecting public-cloud datacenters around the globe

According to current usage patterns, research trends, and latest reports, the performance of the wide-area networks interconnecting geographically distributed cloud nodes (i.e. inter-datacenter networks) is gaining more and more interest. In this paper we leverage only active approaches—thus we do not rely on information restricted to providers—and propose a deep analysis of these infrastructures for the two public-cloud leading providers: Amazon Web Services and Microsoft Azure. Our study provides an assessment of the performance of these networks as a function of the several configuration factors under the control of the customer and evidences specific cases of particular interest. The analysis of these cases and of their root causes, also related with service fees, provides insights on their impact on both the Quality of Service perceived by cloud customers and the outcomes of studies neglecting them.Our results show that Azure inter-datacenter infrastructure performs better than Amazon’s in terms of throughput (+56%, on average). On the other hand, the performance of the two providers is comparable in terms of latency, with the exception of limited specific cases. Moreover, some of the configuration factors cloud customers can leverage (such as larger more expensive VM sizes, advertised to have better network performance) may have no effect on the inter-datacenter network performance actually perceived. Counterintuitively, lower performance may even be related to higher costs for the customer. Experimental evidences show that public-cloud providers also rely on external network providers for some geographical regions, which is the cause of lower performance and higher costs. A comparison with previous works show that TCP throughput has not been improved recently, while evidences of higher link capacities have been found.

A proposed novel enterprise cloud development application model

Currently, there is limited guidance on generic cloud software architecture model for designing and building cloud applications and each platform provider has different standards that influences the way applications are developed and written. In this paper we propose a cloud application development model based on developing services (typically REST API services). We see clear benefits in applying this model to service oriented architecture (SOA) and business process management (BPM) design in order to develop enterprise applications for cloud environments in a systematic, rapid manner. This conceptual model described in this paper is drawn from the core ideas and practices behind REST API, BPM, and SOA. In this paper we focus on the proposed software development model for enterprise cloud applications and services. Throughout this paper we use the term cloud application and cloud service interchangeably. The model described in this paper can be applied to both. Further research on a software-modeling tool that provides a standardized structure for cloud software development and deployment seems to be in order. Supported by a graphical modeling environment, such a tool would utilize a Cloud Resource Provisioning language, will be able to seamlessly deploy and provision cloud resources across multiple cloud service providers. The presented research provides a practical and extensible application development model for cloud platforms. The model described can be used in a variety of applications as well as migration of legacy code to cloud platforms. By using this model, a cloud application can be deployed as small functionally separate components to different cloud nodes. Resource allocation can be defined for these nodes on a per need basis, which minimizes the cost of acquiring a fail over and load balanced system compared to the traditional on premise application development. In this paper, a detailed description of the analysis and development steps of the proposed model is also presented.

JINR cloud infrastructure evolution

To fulfil JINR commitments in different national and international projects related to the use of modern information technologies such as cloud and grid computing as well as to provide a modern tool for JINR users for their scientific research a cloud infrastructure was deployed at Laboratory of Information Technologies of Joint Institute for Nuclear Research. OpenNebula software was chosen as a cloud platform. Initially it was set up in simple configuration with single front-end host and a few cloud nodes. Some custom development was done to tune JINR cloud installation to fit local needs: web form in the cloud web-interface for resources request, a menu item with cloud utilization statistics, user authentication via Kerberos, custom driver for OpenVZ containers. Because of high demand in that cloud service and its resources over-utilization it was re-designed to cover increasing users’ needs in capacity, availability and reliability. Recently a new cloud instance has been deployed in high-availability configuration with distributed network file system and additional computing power.

An Optimization And Security Of Data Replication In Cloud Using Advanced Encryption Algorithm

Cloud computing is an emerging pattern that provides computing, communication and storage resources as a service over a network. In existing system, data outsourced in a cloud is unsafe due to the eaves dropping and hacking process. And it allows minimizing the security network delays in cloud computing. In this paper to study data replication in cloud computing data centers. Unlike another approaches available in the literature, consider both security and privacy preserving in the cloud computing. To overcome the above problem we use DROPS methodology. The data encrypted using AES (Advanced Encryption Standard Algorithm). In this process, the common data are divided into multiple nodes also replicate the fragmented data over the cloud nodes. Each data is stored in a different node in fragments individual locations. We ensure a controlled replication of the file fragments, here each of the fragments is replicated only once for the purpose of improved security. The results of the simulations revealed that the simultaneous focus on the security and performance, resulted in improved security level of data accompanied by a slight performance drop.

A branch and price approach for deployment of multi-tier software services in clouds

This paper considers a service deployment problem that combines service placement and replication level decisions in a cloud computing context. The services are composed of multiple components that are to be placed on nodes in the private cloud of the service provider or, if the private cloud has limited capacity, partly in a public cloud. In the service delivery, the provider has to take into account the quality of service guarantees offered to his end-users. To solve the problem, we develop a branch and price algorithm, where the subproblems both are formulated as a linear mixed integer program and a shortest path problem with resource constraints (SPPRC) on a network with a special structure. The SPPRC can be solved by an exact label-setting algorithm, but to speed up the solution process, we develop a heuristic label-setting algorithm based on a reduced network and simplified dominance rule. Our results show that using the heuristic subproblem solver is efficient. Furthermore, the branch and price algorithm performs better than a previously developed pre-generation algorithm for the same problem. In addition, we analyze and discuss the differences in solutions that utilize resources in a public cloud to different degrees. By conducting this analysis we are able to identify some essential characteristics of good solutions.

CURE—Towards enforcing a reliable timeline for cloud forensics: Model, architecture, and experiments

A malicious alteration of system-provided timeline can negatively affect the reliability of computer forensics. Indeed, detecting such changes and possibly reconstructing the correct timeline of events is of paramount importance for court admissibility and logical coherence of collected evidence. However, reconstructing the correct timeline for a set of network nodes can be difficult since an adversary has a wealth of opportunities to disrupt the timeline and to generate a fake one. This aspect is exacerbated in cloud computing, where host and guest machine-time can be manipulated in various ways by an adversary. Therefore, it is important to guarantee the integrity of the timeline of events for cloud host and guest nodes, or at least to ensure that timeline alterations do not go undetected. This paper provides several contributions. First, we survey the issues related to cloud machine-time reliability. Then, we introduce a novel architecture (CURE) aimed at providing timeline resilience to cloud nodes. Further, we implement the proposed framework and extensively test it on both a simulated environment and on a real cloud. We evaluate and discuss collected results showing the effectiveness of our proposal.

Malware Detection in Cloud Computing Infrastructures

Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over $90$ percent whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions.

Virtual cluster optimisation for MapReduce-like applications

Infrastructure-as-a-service clouds are becoming ubiquitous for provisioning virtual machines on demand. Cloud service providers expect to use the least resources to deliver the best services. As users frequently request virtual machines to build virtual clusters and run MapReduce-like jobs for big data processing, cloud service providers intend to optimise the virtual cluster to minimise network latency and subsequently reduce data movement cost. In this paper, we focus on the virtual machine placement issue for provisioning virtual clusters with minimum network latency in clouds. We define the distance as the latency between virtual machines and use it to measure the affinity of a virtual cluster. Such metric of distance indicates the considerations of virtual machine placement and the topology of physical nodes in clouds. Then, we formulate our problem as the classical shortest distance problem and solve it by building an integer programming model. A greedy virtual machine placement algorithm is designed to get a compact virtual cluster. Furthermore, an improved heuristic algorithm is also presented for achieving a global resource optimisation. The simulation results verify our algorithms and the experiment results validate the improvement achieved by our approaches.

A bio-inspired hybrid algorithm for effective load balancing in cloud computing

Effective scheduling and load balancing are the key challenges of cloud computing technology. A prominent task scheduler must be adaptable to the dynamic distributed environment and to the job scheduling policy based upon the workload. In this research, a novel hybrid algorithm is developed for balancing the load among cloud nodes by hybridising the existing ant colony optimisation (ACO) algorithm and priority-based artificial bee colony (ABC) algorithm where ACO is used for balancing the workload and ABC is used to optimise the resource scheduling from the prospect of cloud computing systems. The proposed technique is tested to show that proposed approach minimise the average response time, average data centre processing time and total processing cost to serve the user requests.

The improved DROP security based on hard AI problem in cloud

Authorising data to administrative control through third-party, is employed in the cloud, gives growth to security concerns. The data mollification may occur due to attacks by other users and nodes within the cloud. Therefore, high safety measures are essential to protect data within the cloud. In this paper, we present a new security primitive based on hard AI problems explicitly novel relations of a graphical secret key system built on top of Captcha technology, which we call Captcha as graphical passwords Carp. CaRP also narrative an approach to address the familiar image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. The DROP methodology fragments the file and replicates the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data that pledges that even if there should be an attack no substantial data is uncovered to the attacker. Furthermore, the nodes storing the fragments are divided with certain expanse by means of graph T-colouring to prohibit an attacker of predicting the locations of the fragments. So, this paper extends DROP methodology with AES algorithm to protect the outsourced data in the cloud and integrate them with CaRP to improve the online security by 14.4%.

A bio-inspired hybrid algorithm for effective load balancing in cloud computing

Effective scheduling and load balancing are the key challenges of cloud computing technology. A prominent task scheduler must be adaptable to the dynamic distributed environment and to the job scheduling policy based upon the workload. In this research, a novel hybrid algorithm is developed for balancing the load among cloud nodes by hybridising the existing ant colony optimisation (ACO) algorithm and priority-based artificial bee colony (ABC) algorithm where ACO is used for balancing the workload and ABC is used to optimise the resource scheduling from the prospect of cloud computing systems. The proposed technique is tested to show that proposed approach minimise the average response time, average data centre processing time and total processing cost to serve the user requests.

Optimal Fragment Selection for Data Security and Efficient Content Alteration Mechanism in Cloud using IABC

Cloud computing is an emerging technology which attracts customers by offering reduced cost, space and virtually unlimited dynamic resources for storage, computation etc. User shares the sensitive data over the cloud which gives rise to security issues in cloud computing. So, to protect user's data a secure methodology of fragmentation and replication of data is used in this paper. Optimal Threshold value is chosen to perform fragmentation of the data file by means of Improved Artificial Bee Colony (IABC) algorithm. Based on the optimal threshold value, data file is fragmented. Once the fragmentation is done, the proposed methodology selects the cloud nodes for fragment placement. The nodes are selected in such a way that the access time is always reduced by giving equal preference to both security and performance in terms of the access time. The centre most nodes are carefully chosen to provide better access time using centrality measure in the cloud network. Based on these measures, our data resides secured in cloud. Replication strategy provides reliable access to data fragments that improves the trustworthiness of the system.

Secure Dynamic Fragment and Replica Allocation in Large-Scale Distributed File Systems

The Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that collectively approaches the security and performance issues. Data to a third-party administrative control, as is done in cloud computing, gives rise to security concerns. The data compromise may occur due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. To protected the data in cloud by using fragmentation and replication. In DROPS methodology, divide a file into fragments, and replicate the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack, no meaningful information is revealed to the attacker.

Performance evaluation of a distributed storage service in community network clouds

SummaryCommunity networks are self‐organized and decentralized communication networks built and operated by citizens, for citizens. The consolidation of today's cloud technologies offers now, for community networks, the possibility to collectively develop community clouds, building upon user‐provided networks and extending toward cloud services. Cloud storage, and in particular secure and reliable cloud storage, could become a key community cloud service to enable end‐user applications. In this paper, we evaluate in a real deployment the performance of Tahoe least‐authority file system (Tahoe‐LAFS), a decentralized storage system with provider‐independent security that guarantees privacy to the users. We evaluate how the Tahoe‐LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network such as Guifi.net. Furthermore, we evaluate Tahoe‐LAFS in the Microsoft Azure commercial cloud platform, to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe‐LAFS. We observed that the write operation of Tahoe‐LAFS resulted in similar performance when using either the community network cloud or the commercial cloud. However, the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe‐LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe‐LAFS can run on community network clouds with suitable performance for the needed end‐user experience. Copyright © 2015 John Wiley & Sons, Ltd.

MN-MATE

Recent advent of manycore system increases needs for larger but faster memory hierarchy. Emerging next generation memories such as on-chip DRAM and nonvolatile memory (NVRAM) are promising candidates for replacement of DRAM-only main memory. Combined with the manycore trends, it gives an opportunity to rethink conventional resource management system with a memory hierarchy for a single cloud node. In an attempt to mitigate the energy and memory problems, we propose MN-MATE, an elastic resource management architecture for a single cloud node with manycores, on-chip DRAM, and large size of off-chip DRAM and NVRAM. In MN-MATE, the hypervisor places consolidated VMs and balances memory among them. Based on the monitored information about the allocated memory, a guest OS co-schedules tasks accessing different types of memory with complementary access intensity. Polymorphic management of DRAM hierarchy accelerates average memory access speed inside each guest OS. A guest OS reduces energy consumption with small performance loss based on the NVRAM-aware data placement policy and the hybrid page cache. A new lightweight kernel is developed to reduce the overhead from the guest OS for scientific applications. Experiment results show that our techniques in MN-MATE platform improve system performance and reduce energy consumption.

Design adaptive task allocation scheduler to improve MapReduce performance in heterogeneous clouds

This paper conducted a thorough research on one of the critical technologies in cloud computing, the MapReduce programming model. Some of the past research results showed that one of the methods for enhancing MapReduce performance is the execution through allocating identical tasks to each cloud node. However, such allocation is not applicable for the environment of a heterogeneous cloud. Due to the different computing power and system resources between the nodes, such uniform distribution of tasks will lower the performance between nodes, and hence this paper makes improvements on the original speculative execution method of Hadoop (called Hadoop Speculative) and LATE Scheduler by proposing a new scheduling scheme known as Adaptive Task Allocation Scheduler (ATAS). The ATAS adopts more accurate methods to determine the response time and backup tasks that affect the system, which is expected to enhance the success ratio of backup tasks and thereby effectively increase the system’s ability to respond. We conducted three different simulation experiments, in which execution time of the LATE Scheduler and ATAS could be reduced by a maximum of 12% and 30%, increased the average tasks throughput by a maximum of 13% and 33%, and reduced the average tasks latency by a maximum of 25% and 36% compared to Hadoop Speculative; the ATAS can effectively enhance the processing performance of MapReduce to be compared with LATE Scheduler in a heterogeneous cloud environment.

A novel approach on weight based optimized routing for mobile cloud computing

The combination of mobile communications and cloud computing are growing immensely popular. Application designers and architects use cloud technology advantages to deliver data and information to mobile devices as swiftly and seamlessly as possible. However, a critical aspect in this scenario is the factor of routing. Complex routing techniques can use up a lot of energy. Coupled with an increasing number of cloud data centers, this has the potential to leave a negative impact on our environment. An efficient routing model can help lower carbon emissions in the long run. Most existing routing methods involve shortest path routing to ease congestion in mobile networks. Through this paper, the authors examine the shortcomings of such existing protocols. The authors discuss mobile base stations and how routing takes place. An algorithm is presented which illustrate the application of the proposed model as presented in this paper. The proposed model is introduced by the authors in this work as Weight-Based Optimized Routing. Using this new approach, the authors demonstrate a method to lower network congestion and ensure a more smooth data exchange between mobile devices and cloud nodes. A reduction in mobile network congestion situations and enhancement of data transmission rates are vital towards achieving a green cloud computing environment.

Performance Analysis of Load Balancing Algorithms in Cloud Computing

computing is a business oriented IT-technology, which is composed of multiple computing technologies accessed via internet. With the rapid increase in cloud usage, it becomes a challenge to deliver the cloud services effectively and efficiently to the cloud consumers on the pay-per usage basis. In this concern Balancing of load has become one of the essential components for the cloud computing environment to perform the effective operations. Scheduling of virtual machines or data centers has to be done properly by using an appropriate load balancing technique. Hence, several algorithms have been developed to process the client's request towards the cloud nodes.. In this present work, a hybridized swarm intelligence technique is proposed to evenly distribute the incoming task requests among the virtual machines or server. Additionally, the performance analysis has been performed using the CloudAnalyst simulator. This paper gives a comprehensive performance analysis of the proposed approach and compares its results with existing Round Robin (RR), Equally Spread Current Execution (ESCE) and ant colony optimization (ACO) techniques. Simulation results have demonstrated that the proposed technique shows a significant outcome in terms of response time, data center processing time and total cost in cloud computing.

CloudAnalyst : A Survey of Load Balancing Policies

ABSTRACT Cloud computing is one of the incredible technology which enable the new vision for IT industry. Nowadays, it has become a strong alternative for startup large as well as small scale organizations that only use the resources which actually required based on pay as per use. As Cloud Computing is growing continuously and clients from different parts of the world are demanding for the various services and better outcomes, the load balancing has become the challenge for the cloud provider. To accurately manage the available resources of the different cloud provider, resources have to be properly selected according to the properties of task. Many algorithms have been proposed to provide efficient mechanisms and assigning the client’s requests to available cloud nodes and aim to enhance the overall performance of the cloud and provide more satisfaction to user and efficient services. Initially this paper gives an introduction to cloud computing and load balancing. A detailed survey on different load balancing policy in cloud analyst, their advantages and drawback with obtainable solution and learn how to add new policy or customize existing load balancing policy.

Mapping of time-consuming multitask applications on a cloud system by multiobjective Differential Evolution

The paper deal with the mapping problem of multitask parallel applications on cloud systems.Specific reference is made to task interaction graph applications.A new form of service contract is hypothesized.A tool based on a multiobjective Differential Evolution algorithm is proposed to deal with the problem.The results demonstrate the effectiveness of the proposed evolutionary mapper. Cloud computing is on-demand provisioning of virtual resources aggregated together so that by specific contracts users can lease access to their combined power.Here we hypothesize a new form of service contract by means of which users do not explicitly require resources, but simply supply information about their time-consuming multitask applications and specify their needs through some quality of service (QoS) parameters. The individuation of the virtual machines (VMs) onto which map and execute them is left to the cloud manager. Unfortunately the task/node mapping, already known as NP-hard for conventional parallel systems, becomes more challenging when application tasks must be run on VMs hosted on heterogeneous and shared cloud nodes, and when it must comply with QoS requests too. To support this new cloud service, a novel mapper tool, based on a multiobjective Differential Evolution algorithm, is proposed. Such a tool defines the mapping of the tasks on the VMs with the aim to exploit as much as possible the available cloud resources without penalizing the execution time of the submitted applications and, at the same time, to respect users' QoS requests.To reveal the robustness of this evolutionary tool, an experimental analysis on artificial time-consuming parallel applications, modeled as task interaction graphs, has been effected.