AnomalyDetect: Anomaly Detection for Preserving Availability of Virtualized Cloud Services

Over the Virtual Top. Digital Service Value Chain Disintermediation Implications for Hybrid Heterogeneous Network Regulation

Cloud computing is one of the most important business models of modern information technology. It provides a minimum of various services to the user interaction and low cost (hardware and software). Cloud services are based on the newline architectures on virtualization by using the multitenancy for better resource management and newline strong isolation between several virtual machines (VMs). The spying on a victim VM is challenging, particularly when one wants to use per‐core microarchitectural features as a side channel. For example, the cache contains the most potential for damaging side channels, but shared information across different cores affects the cloud information. To overcome this problem, propose the Secure Block‐Level Double Encryption (SBLDE) algorithm for user signature verification in the cloud server. It uses identity‐based detection techniques to monitor the colocated VMs to identify abnormal cache data and channel behaviors typically during VM data transformation. The identity‐based linear classification (IBLC) method is used for classifying the attacker channel when the data is transferred/retrieved from the VM cloud server. This cloud controller finds the channel misbehavior to block the port or channel, changing other available ports’ communication. The service verification provides strong user access permission on the cloud server when the unknown request to the cloud server suddenly executes the key authentication to verify the user permission. This linear classification trains the existing side‐channel attack datasets to the classifier and identifies the VM cloud’s attack channel. The study focused on preventing attacks from interrupting the system and serves as an effective means for cross‐VM side‐channel attacks. This proposed method protects the cloud data and prevents cross‐VM channel attack detection efficiently, compared to other existing methods. In this overall proposed method, SBLDE’s performance is to be evaluated and then compared with the existing method.

IaaS clouds offer customers on-demand computing resources such as virtual machine, network and storage. To provision and manage these resources, cloud users must rely on a variety of cloud services. However, a wide range of vulnerabilities have been identified in these cloud services that may enable an adversary to compromise customers' computations or even the cloud platform itself. Using the motivation for adding mandatory access to commercial operating systems, we argue for the development of a secure cloud operating system (SCOS) to enforce mandatory access control (MAC) over cloud services and customer instances. To better understand the concrete challenges of building a SCOS, we examine the OpenStack cloud platform from two perspectives: (1) how attacks propagate across cloud services and (2) how adversaries leverage vulnerabilities in cloud services to attack hosts. Using this information, we review the application of three MAC approaches employed by "secure" commercial systems to evaluate their practical effectiveness for controlling cloud services. While MAC enforcement can improve security for cloud services, several threats remain unchecked. We outline a set of additional security policy goals that a SCOS must enforce to control threats from potentially compromised cloud services comprehensively. While we do not actually construct a SCOS in this paper, we hope that this study will initiate discussions that may lead to practical designs.

Virtual machine (VM) is an important mechanism to multiplex computer resources. The increasing popularity of network computing has renewed research interests in the adaptive and distributed virtual machines. Service migration is a vital technique to construct reconfigurable VMs. By incorporating mobile agent technology, VM systems can improve their resource utilization, load-balancing and fault-tolerance significantly. This work focuses on the decision problem of hybrid mobility for load-balancing in reconfigurable distributed VMs. We tackle this problem from three aspects: migration candidate determination, migration timing and destination server selection. The service migration timing and destination server selection are formulated as two optimization models. We derive the optimal migration policy for distributed and heterogeneous systems based on stochastic optimization theories. Renewal processes are applied to model the dynamics of migration. We solve the agent migration problem by dynamic programming and extend the optimal service migration decision by considering the interplay of the hybrid mobility. Our decision policy is complementary to the existing service and agent migration techniques. Its accuracy is verified by simulations.

In this paper, we study the problem of dynamically migrating a service in Clouds to satisfy a sequence of mobile batch-request demands in a cost effective way. The service may have a single or multiple replicas, each running on a virtual machine. As the origins of the mobile accesses are frequently changed over time, this problem is particularly important to those time-bounded services to achieve enhanced QoS and cost effectiveness. Moving services closer to client locations not only reduces the service access latency but also minimizes the network cost for service providers. However, these benefits do not come without compromise. The migration comes at cost of bulk data transfer and service disruption, as a result, increasing the overall service costs. To gain the benefits of service migration while minimizing the increased monetary costs, we propose a search-based dynamic migration algorithm that can effectively migrate a single or multiple servers to adapt to the changes of access patterns with minimum service costs. The algorithm is characterized by effective uses of historical access information to conduct virtual moves of a set of servers as a whole under a certain condition so as to overcome the limitations of local search in cost reduction. Our simulation results show that the proposed algorithm can effectively achieve the goal by satisfying service request sequences. Moreover, with moderate migration cost, using a single server is more cost-effective to satisfy the requests in Clouds than deploying multiple servers.

As cloud services become mature and provide capabilities for elastic computation and data management, hosting services become increasingly prevalent such as Amazon Web Services, Microsoft Azure, or RedHat OpenStack. Workloads are hosted in these platforms using hypervisors running on physical machines which host virtual machines (VMs) presented to the customer. Software-defined network (SDN) technologies move many of the network functions into the hosts. Accordingly SDN-based VM placements have received significant attention as a way to minimize network traffic between VMs and hosts. Careful placement of VMs can generate a significantly lower amount of network traffic to the hosting provider. Further, it may impact the level of security risk exposed by the VM placement as each VM may have a different level of security risk. In this work, we consider these two conflicting goals by presenting a Software-Defined Network based VM Placement, called SDNVMP, to minimize both network traffic between VMs (and hosts) and security risk by grouping VMs with similar security scrutiny needs. Our results show that the proposed placement algorithm outperforms the baseline counterparts in terms of network traffic overhead and security risk in each VM introduced by such algorithms.

Infrastructure as a Service (IaaS) clouds are gaining increasing popularity as a platform for distributed computations. The virtualization layers of those clouds offer new possibilities for rapid resource provisioning, but also hide aspects of the underlying IT infrastructure which have often been exploited in classic cluster environments. One of those hidden aspects is the network topology, i.e. the way the rented virtual machines are physically interconnected inside the cloud. We propose an approach to infer the network topology connecting a set of virtual machines in IaaS clouds and exploit it for data-intensive distributed applications. Our inference approach relies on delay-based end-to-end measurements and can be combined with traditional IP-level topology information, if available. We evaluate the inference accuracy using the popular hyper visors KVM as well as XEN and highlight possible performance gains for distributed applications.

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

Impact of Factors Affecting Pre-copy Virtual Machine Migration Technique for Cloud Computing

The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be designed. One specific area where energy efficient algorithms are required is virtual machine consolidation. With virtual machine consolidation, the objective is to utilize the minimum possible number of hosts to accommodate the required virtual machines, keeping in mind the service level agreement requirements. This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host. The online algorithm is analyzed using a competitive analysis approach. In addition, an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms. Our proposed online algorithm consumed 25% less energy and performed 43% fewer migrations than the benchmark algorithms.

Virtual machine cluster mobility in inter-cloud platforms

Infrastructure as a Service (IaaS) Cloud is emerging as a popular utility computing model in which computational resources like processor, memory, storage etc. are provided on demand. Virtual Machines (VMs) are used to provide on demand virtualized resources. Several VMs may run parallely on a physical machine depending on the demand. VM placement and VM resource allocation are two important problems in an infrastructure cloud. In such systems, performance interference may become a mutual problem between a VM which is booting and a VM which is coexisting, thus affecting the boot-time of a VM as well as response time of the coexisting VMs. The work presented here studies the impact of computational resources available on boot-time of a VM. Our experiments suggest to add a special server known as VMBooter inside an infrastructure cloud. Experimental results have shown considerable improvement in VM boot-time with lowering down the impact of performance interference on coexisting VMs with the help of VM migration.

Data establishment and resource provision are the most crucial tasks in the data center. To achieve minimum service latency, it is required to have a balance between the virtual machine and physical machine for proper execution of any query into the cloud data center. Cloud services have a huge market in the world trade. These services have a large impact on every field, also on research. Latency is a major problem in the growth of the cloud market in a real time scenario. Online trade, marketing and banking have a large market of cloud services, which require minimum latency in the real-time response otherwise the whole market would be destroyed. Latency prediction plays a crucial role in managing the load on the data center. To perfectly maintain a request waiting queue, it is required to predict accurate latency between the virtual machines in the data center. If any approach can predict accurate latency in the data center for any particular request, then it can perfectly manage the waiting queue for the cloud data center. Thus, prediction plays a crucial role in reducing latency in the execution of any request to the cloud data center. This article presents an online latency prediction approach for VMs to improve load balancing. A Euclidean Circle Steiner Tree point is proposed. Results show compression with existing mechanisms and get 8-12 % more accuracy in latency prediction.

Most of the businesses now-a-days have started using cloud platforms to host their software applications. A Cloud platform is shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end user's requirements. The cloud providers must ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. Auto-scaling supports the cloud service providers achieve the goal of supplying the required resources automatically. It use methods that will calculate the number of requests and decides the resources to release based on workload. The workload consists of some quantity of application program running on the machine and usually some number of users connected to and communicating with the computer's applications. The researchers have used various approaches to perform autoscaling which is a process to predict the workload that is required to handle the end users request and provide required resources as Virtual Machines (VM) disruptively. Along with providing uninterrupted service, the businesses also only pay for the service they use, thus increasing the popularity of Cloud computing. Based on the workload identified the resources are provisioned. The resource provisioning techniques is a model used for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications, and services) required resources are released. In this regard, the aim of this paper is to develop a framework to predict the workload using deep learning which would be able to handle provisioning of cloud resources dynamically. This framework would handle the user request efficiently and allocate the required virtual machines. As a result, an efficient dynamic method of provisioning of cloud services would be implemented supporting both the cloud providers and users.

With the rapid development of cloud advancement, a data security challenge has emerged. In this paper, a technique based on elliptical cryptography and cuckoo search algorithm is proposed. With this technique, data owners securely store their data files in the cloud server. Initially the user sends a file storage request to store a file in a cloud server provider (CSP). The input file is checked whether it is sensitive or non-sensitive by the user. If the file is sensitive, then it would be split and stored in different virtual machines (VMs), and if the file is non-sensitive, then it would be assigned in a single VM. This approach was used for the first time as per the survey. To add further security, the sensitive data retrieval needs an encryption process that is supported by the proposed algorithm. If the data owner stores the sensitive data to cloud server, the data owner's document is encrypted by the double encryption technique. Here RSA and optimal elliptic curve cryptography (OECC) algorithm is used to encrypt the document with high security. The authors have used cuckoo search algorithm to identify the optimal key in ECC. This paper has proposed a novel cryptography approach for delivering mass distributed storage by which user's original data cannot be directly reached by cloud operators. Hence, this research has proved that the proposed work will give better securable data storage solving the security issues.