Azizol Abdullah

A Survey on Resource Allocation and Monitoring in Cloud Computing

The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing. On the other hand, the resources on the cloud are pooled in order to serve multiple subscribers. The provider use multi-tenancy model where the resources (physical and virtual) are reassigned dynamically based on the tenant requirement (5). The assigning of the resources will be based on the lease and SLA agreement, whereby different clients will need more or less amount of virtual resources. Subsequently, the growth of demands for cloud services is bringing more challenge for the provider to provide the resources to the client subscriber. Therefore, in this paper we provide a review on cloud computing which focus on resource management: allocation and monitoring. Our methodologies for this review are as follows:  We provide a cloud computing taxonomy covers the cloud definitions, characteristics and deployment models.  We then analyze the literatures and discuss about resource management, the process and the elements.  We then concentrate literatures on resource allocation and monitoring. We derived the problems, challenge and the approach solution for resource allocation and monitoring in the cloud. This paper organizes as follows: Section II introduces an overview of Cloud Computing, Section III discuss about resource management and its processes, Section IV discuss about related work with resource management in the cloud, section, Section V describes about approach solution to resource allocation and monitoring and, finally Section VI concludes the paper.

Disaster Recovery in Cloud Computing: A Survey

Disaster recovery is a persistent problem in IT platforms. This problem is more crucial in cloud computing, because Cloud Service Providers (CSPs) have to provide the services to their customers even if the data center is down, due to a disaster. In the past few years, researchers have shown interest to disaster recovery using cloud computing, and a considerable amount of literature has been published in this area. However, to the best of our knowledge, there is a lack of precise survey for detailed analysis of cloud-based disaster recovery. To fill this gap, this paper provides an extensive survey of disaster recovery concepts and research in the cloud environments. We present different taxonomy of disaster recovery mechanisms, main challenges and proposed solutions. We also describe the cloud-based disaster recovery platforms and identify open issues related to disaster recovery.

Hierarchical secure virtualization model for cloud

Cloud services are providing on-demand resources via virtualization technologies. This will make cloud computing a potential target for cyber attacks. Most proposed security models for virtualization are working above virtualization on host OS. Almost all proposed cloud security models suffers from this problem in that these models have very limited control over virtualization. In this paper a Hierarchical Secure Virtualization Model (HSVM) is proposed to provide threat quarantine and conquer in addition to complete control on virtualization. HSVM needs to be implemented under the virtualization level and eventually moving up to the guest OS. This security model has the potential to protect various cloud service models implemented by cloud vendors, such as IaaS, PaaS, dSaaS, and SaaS, and improves cloud vendor control level in IaaS. To the best of our knowledge, there is no similar model or implementation like HSVM, able to protect the cloud from DDoS attack, unauthorized access, data leakage as well.

A Review of Bring Your Own Device on Security Issues

Mobile computing has supplanted internet computing because of the proliferation of cloud-based applications and mobile devices (such as smartphones, palmtops, and tablets). As a result of this, workers bring their mobile devices to the workplace and use them for enterprise work. The policy of allowing the employees to work with their own personal mobile devices is called Bring Your Own Devices (BYOD). In this article, we discuss BYOD’s background, prevalence, benefits, challenges, and possible security attacks. We then review contributions of academic researchers on BYOD. The Universiti Putra Malaysia online databases (such as IEEE Xplore digital library, Elsevier, Springer, ACM digital library) were used to search for peer-reviewed academic publications and other relevant publications on BYOD. The Google Scholar search engine was also used. Our thorough review shows that security issues comprise the most significant challenge confronting BYOD policy and that very little has been done to tackle this security challenge. It is our hope that this review will provide a theoretical background for future research and enable researchers to identify researchable areas of BYOD.

Quorum-based Data Replication in Grid Environment

Replication is a useful technique for distributed database systems and can be implemented in a grid computation environment to provide a high availability, fault tolerant, and enhance the performance of the system. This paper discusses a new protocol named Diagonal Data Replication in 2D Mesh structure (DR2M) protocol where the performance addressed are data availability which is compared with the previous replication protocols, Read-One Write-All (ROWA), Voting (VT), Tree Quorum (TQ), Grid Configuration (GC), and Neighbor Replication on Grid (NRG). DR2M protocol is organized in a logical two dimensional mesh structure and by using quorums and voting techniques to improve the performance and availability of the replication protocol where it reduce the number of copies of data replication for read or write operations. The data file is copied at the selected node of the diagonal site in a quorum. The selection of a replica depends on the diagonal location of the structured two dimensional mesh quorum where ...

Cooperation stimulation mechanisms for wireless multihop networks

In wireless multihop networks such as wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs), nodes have to rely on their peer neighbours in transmitting packets to intended destinations. A successful rate of communication in these networks is assured if all nodes in the network fully cooperate to relay packets for each other. However, due to the existence of nodes with various motives, cooperativeness cannot be ensured and the communication goal is not achieved. Consequently, many cooperation stimulation approaches have been proposed to address node selfishness by using, broadly, incentive-based and punishment-based approaches. These schemes consist of several components including monitoring mechanisms, that need to be optimized in order to provide effective ways to detect and manage selfish nodes in the networks. This paper summarizes existing cooperation stimulation mechanisms and discusses important issues in this field such as false judgment and node collusion, whereby the root of these kinds of problems originates from the inability to obtain accurate evaluation on the behaviour of a node.

Decentralized replication strategies for P2P based Scientific Data Grid

Scientific Data Grid provides geographically distributed resources for large-scale data-intensive applications that generate large scientific data sets and it mostly deals with large computational problems. Research in the area of grid has given various ideas and solutions to address these requirements. However, since the number of participants (scientists and institutes) that involve in this kind of environment is increasing tremendously, scalability, availability and reliability have been the core problem for such system. Peer-to-peer (P2P) is one of the architecture that promising scale and dynamism environment. In this paper, we present a P2P model for Scientific Data Grid that utilizes the P2P services to address those problems. For the purpose of this study, we have developed and used our own data grid simulation written using PARSEC. In this paper, we illustrate our P2P Scientific Data Grid model, our data grid simulation and the design of proposed data replication strategies. We then analyze the performance of data discovery service with and without the existence of replication strategies relative to their success rates, response time, average number of hop and bandwidth consumption. The results from simulation study that show how the proposed replication strategies promote high data availability in the proposed Scientific Data Grid model and how these strategies improve the discovery process are presented.

An effective approach for managing power consumption in cloud computing infrastructure

Cloud computing offers a dynamic provisioning of server capabilities as a scalable virtualized service. Big datacenters which deliver cloud computing services consume a lot of power. This results in high operational cost and large carbon emission. One way to lower power consumption without affecting the cloud services quality is to consolidate resources for reducing power. In this paper, we introduce a DNA-based Fuzzy Genetic Algorithm (DFGA) that employs DNA-based scheduling strategies to reduce power consumption in cloud datacenters. It is a power-aware architecture for managing power consumption in the cloud computing infrastructure. We also identify the performances metrics that are needed to evaluate the proposed work performance. The experimental results show that DFGA reduced power consumption when comparing with other algorithms. Our proposed work deals with real time task which is not static, and concentrates on the dynamic users since they are involved in cloud.

A particle swarm optimization and min---max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids

In service-orientated grids (SOG) environments, grid workflow schedulers play a critical role in providing quality-of-service (QoS) satisfaction for various end users (EUs) with diverse QoS objectives and optimization requirements. The EU requirements are not only many and conflicting, but also involve constraints of various degrees—loose, moderate or tight. However, most of the existing scheduling approaches violate EU constraints in tight situations and suffer inferior QoS optimization results. In this paper, a constraints-aware multi-QoS workflow scheduling strategy is proposed based on particle swarm optimization (PSO) and a proposed look-ahead heuristic (LAPSO) to improve performance in such situations. The algorithm selects the best scheduling solutions based on the proposed constraint-handling strategy. It hybridises PSO with a novel look-ahead mechanism based on a min–max heuristic, which deterministically improves the quality of the best solutions. Extensive simulation experiments have been carried out to evaluate the performance of the proposed approach. The simulation results show that the LAPSO algorithm guarantees satisfaction (0% violation) of the EU constraints even in tight situations. It also outperforms the comparison algorithm, with about 30% increase, in terms of cumulative QoS satisfaction of optimization requirements. In addition, the new scheme significantly reduces the CPU time by about 75% compared to the benchmark algorithm.

Distributed Denial of Service detection using hybrid machine learning technique

Distributed Denial of Service (DDoS) is a major threat among many security issues. To overcome this problem, many studies have been carried out by researchers, however due to inefficiency of their techniques in terms of accuracy and computational cost, proposing an efficient method to detect DDoS attack is still a hot topic in research. Current paper proposes architecture of a detection system for DDoS attack. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are deployed for feature selection and attack detection respectively in our hybrid method. Wrapper method using GA is deployed to select the most efficient features and then DDoS attack detection rate is improved by applying Multi-Layer Perceptron (MLP) of ANN. Results demonstrate that the proposed method is able to detect DDoS attack with high accuracy and deniable False Alarm.