An Efficient Decentralized Energy and Communication Aware Workload Management

The security of cloud services and underlying resources is a major concern due to vulnerabilities existing in current implementation of the virtualization. Thus, there is a need of detecting system-level attacks like viruses, worms, malware, etc. In this paper, we extend our previous work on vulnerability assessment and patching by integrating in-VM-assisted agent-based malware detection (AMD) framework for securing high-risk virtual machines (VMs) in cloud. The proposed framework has two components, viz. agent at VM and anomaly detection at hypervisor. An agent continuously looks for the new deployment of the executable in-VM and applies the signature-based detection to detect known malware. For detecting unknown attacks, it generates the profile with optimal static features for new executable. The optimal features are derived using an extended binary bat algorithm with two new fitness functions. The profile is transferred to hypervisor where anomaly detection using random forest classifier is applied. It classifies the executable to either normal or malware and generates an alert to VM user. The functionality of the proposed AMD framework is validated over cloud testbed at NIT Goa, as well as with the latest malware datasets. In addition, we analyze the VM security requirements fulfilled by the proposed framework.

In Infrastructure-as-a-Service clouds, there exist many virtual machines (VMs) that are not used for a long time. For such VMs, many vulnerabilities are often found in installed software while VMs are suspended. If security updates are applied to such VMs after the VMs are resumed, the VMs easily suffer from attacks via the Internet. To solve this problem, offline update of VMs has been proposed, but some approaches have to permit cloud administrators to resume users' VMs. The others are applicable only to completely stopped VMs and often corrupt virtual disks if they are applied to suspended VMs. In addition, it is sometimes difficult to accurately emulate security updates offline. In this paper, we propose OUassister, which enables consistent offline update of suspended VMs. OUassister emulates security updates of VMs offline in a non-intrusive manner and applies the emulation results to the VMs online. This separation prevents virtual disks of even suspended VMs from being corrupted. For more accurate emulation of security updates, OUassister provides an emulation environment using a technique called VM introspection. Using this environment, it automatically extracts updated files and executed scripts. We have implemented OUassister in Xen and confirmed that the time for critical online update was largely reduced.

Nowadays, review mining is very popular. People usually want to go for the reviews first rather than directly going to the market and purchase the product or paying for a service. However, it is very difficult to get the best one until making the comparison between two or more positively categorized products. This work highlights a platform where review mining become very easy and automated. The automation of crawling is done through various websites for a product or service and downloading their reviews at their own. The model is deployed in the Cloud environment for executing the multiple websites in parallel, which speed up the process to meet the requirements of real time recommendations. Experiments are performed on various product reviews datasets in order to provide and analyze the reviews in the form of star rating score scaling from 0–5.

Performance analysis of local, network and distributed file systems running inside user’s virtual machines in cloud environment

The virtual machine placement problem can be described as designing optimal placement scheme for virtual machine in cloud environment. Cloud data centers are facing increasingly virtual machine placement problems, such as high energy consumption, imbalanced utilization of multidimensional resource, and high resource wastage rate. In this paper, typical exact and heuristic algorithms as solution to the virtual machine placement problem in the cloud are surveyed in terms of energy consumption and resource wastage. The purpose of this paper is to evaluate the performance of both the exact and approximate algorithms developed by using the WebCloudSim sytem.

The rapid development of cloud computing expands the scale of modern cloud data centers, resulting in growing challenges of energy consumption. Load balancing of virtual machines(VMs), for the purposes of improving the utilization of physical resources and reducing energy consumption, has become a research focus in recent years. However, the existing researches mostly focus on how to maximize resource utilization and reduce energy consumption. Security issues in the context of load balancing of VMs were rarely addressed. In this paper, we research the key procedures of load balancing, VM selection and VM placement, we find that the existing schemes introduced several security problems. In consideration of some conclusions of recent researches and inevitable live migrations during load balancing, common tenants have sufficient reasons to worry about their VMs' security when they are migrated to strange hosts and/or co-reside with the VMs owned by strange tenants. In short, VMs' mobility introduced by load balance expands the attack surface. In this work, we classify and analyze related security threats and create an information leakage model for load balancing. We present a new security policy, SeLance, to secure the load balancing via avoiding above threats as far as possible. We develop exact implementations in CloudSim and OpenStack. We show that SeLance can effectively alleviate the threats introduced by load balancing, the security score can improve 46.90%-81.15%, while keeping the load balancer's original function to a great extent (± 2.5%).

In Infrastructure-as-a-Service (IaaS) clouds, users manage the systems in virtual machines (VMs) called user VMs through remote management systems (RMSes). To allow users to manage their VMs during failures inside the VMs, IaaS usually provides out-of-band remote management. This management is performed indirectly via an RMS server in a privileged VM called the management VM. However, it is discontinued when a user VM is migrated. This is because an RMS server in the management VM at the source host is terminated on VM migration. Even worse, pending data is lost between an RMS client and a user VM. In this paper, we propose D-MORE for continuing out-of-band remote management across VM migration. D-MORE provides a privileged and migratable VM called DomR and performs out-of-band remote management of a user VM via DomR. During VM migration, it synchronously co-migrates DomR and its target VM and transparently maintains the connections between an RMS client, DomR, and its target VM. We have implemented D-MORE in Xen and confirmed that a remote user could manage his VM via DomR after the VM has been migrated. Our experiments showed that input data was not lost during VM migration and the overhead of D-MORE was acceptable.

In general, operating systems (OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same configuration. Applications in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine (VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, vSpec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. vSpec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of vSpec on benchmarks and real-world applications.

WITHDRAWN: Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment

This paper presents ALARM, an autonomic load-aware resource management algorithm that can be used to manage physical machines or virtual machines in cloud, which participate in a P2P key-value store. A lot of existing key-value stores claim that they are elastic enough to scale up or down with no downtime or interruption to applications. However, the question that when the scaling up or down should take place has still not been resolved. The situation may get worse if the data store consists of hundreds of machines, for it's unrealistic for a system administrator to monitor the system and add/remove a machine manually. Fortunately, cloud computing and virtualization technology have enabled the real-time provision of virtual machines and a way of managing virtual machines without human interference. By supervising the utilization of multiple resources (CPU, memory, network IO, etc.) in virtual machines hosting the data store, our ALARM algorithm will take effect when some of the machines become overloaded or under loaded. The experiment result shows that ALARM helps the Open Chord data store, an open-source implementation of the Chord protocol, scale up and down according to the resource usage in the virtual machines.

Service level agreement (SLA) is a contract between cloud computing customer and provider. Improving the customers' trust with cloud computing requires monitoring and ensuring the quality of the provided services continuously. Therefore, this paper provides a model for monitoring the SLA which belongs to the same customer and a specific provider of multiple virtual machines (VMs) in the same environment. The core foundations for this model are: SLA, quality of service (QoS) parameters, multiple VMs and violations' penalty assessment. The proposed model monitors the QoS parameters within multiple VMs environment dynamically and checks for any violation. Moreover; the penalty for each violation will be calculated and informed to the customer and provider at the same time. The proposed model depends on the time factor while running the VMs in sequentially manner with a short period of time as a gap between each VM. This model prevents any interception between VMs and can maintain synchronisation.

Virtual Infrastructure-as-a-Service (IaaS) clouds are emerging for secondary cloud service providers to manage their own IaaS clouds on top of existing IaaS clouds. In virtual IaaS clouds, guest virtual machines (VMs) run inside cloud VMs provided by existing IaaS clouds. Unlike traditional IaaS clouds, they can be migrated between cloud VMs co-located at the same host. However, the performance of such VM migration is low due to slow virtual networks and doubled system loads. To optimize VM migration between co-located cloud VMs, we propose zero-copy migration for virtual IaaS clouds. Zero-copy migration just relocates the memory image of a guest VM without any copy. To enable live migration with negligible downtime, it first makes the memory of a guest VM share with the destination cloud VM and thereafter completes memory relocation. We have implemented a system called VMBeam for enabling zero-copy migration in Xen. According to our experimental results, zero-copy migration could achieve high migration performance and low system loads.

Optimizing dynamic survivability and security of replicated data in cloud systems under co-residence attacks

In this research, an Individual Learner Big Data Based Cloud Role Player Game Learning Corresponding to Individual Learner Big Data is proposed. Based on the proposed platform, all the learning states and information (Big Data) of individual learner can be further analyzed. By using dynamic assigni ng algorithm, individual learner (student) can be separated into different levels related to the learning ability. According to dynamic assigning algorithm, student's answering times and answering correct rate of each education scenario in the proposed cloud based digital game learning system is considered. Then, suitable teaching scenario will be provided for individual user corresponding to personal learning ability. Each designed modular teaching scenario can be embedded into the game platform on the virtual machine in cloud. The information about students in the game learning system can be stored in the cloud database. In addition, the whole learning system is established as a virtual machine. System maintainer can configure the learning system easily and quickly. Based on cloud, different remote devices can connect to server for learning.

Performance analysis of virtual machines is an indispensable capability for achieving on-demand resource provisioning in cloud computing systems. In this paper, a nonparametric multivariate method is presented as a solution to forecast performance degradation of virtual machines in cloud computing systems. Firstly, the k-means algorithm is adopted to partition multivariate training data into three clusters, which correspond to the virtual machine states of normal, anomaly and failure. Based on this clustering, the performance data of working virtual machines are classified. For those classified as anomalies, a nonparametric CUSUM algorithm is carried out to analyze whether they will lead to serious performance degradation (corresponding to the failure state). Experiment results based on Hadoop show this method can not only identify the normal and failure states of virtual machines, but also succeed in forecasting performance degradation of virtual machines by those anomalous data.