Insecure Until Proven Updated

The recent increase in the use of cloud service has led to the need for cloud service broker(CSB) for supporting the use of various cloud computing platforms. CSB plays the role of not only providing overall services needed for individuals and companies to use cloud computing but also managing and inspecting them. As cloud computing became commonplace, it is expected that the demand for more useful cloud-based service at more affordable prices will increase exponentially. Accordingly, the current situation calls for CSB that connects cloud service provider & customer and provides the service of managing and inspecting cloud service. This paper presents a framework of the components and role of cloud service broker system that supports the allocation, control and management of virtual system based on brokering function between cloud service provider(CSP) and cloud service customer (CSC) by integrating and managing cloud resources in multiple heterogeneous cloud environment. Based on this, it is expected that this paper will contribute to the revitalization of cloud service industry.

Cloud Computing is the recent technology that is based on shared pool of resources and provides features like Ubiquitous access, multi-tenancy, flexibility, scalability and pay as you use, which makes it more resource efficient and cost effective. But Cloud-based systems open unfamiliar threats in authentication and authorization. Explicit authorization accordance must be defined at smallest level, especially in multi-tenant environments. The liaison between Cloud Service Provider & customer must also be clearly mentioned in relation like who holds administrative rights and indirect access to privileged customer information. Moreover the scenario of cloud in educational and research community is still developing and has some security concerns. This paper provides a brief review about Cloud Security concerns for adoption of cloud computing in data sensitive research and technology aided education. Also this paper proposes, ECK based framework for securing end-user data in Community Cloud. Implications and considerations for additional research are provided as well.

With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestation architecture. CloudTA groups all virtual machines on each cloud server and introduces an integrity measurement group (IMG) to measure virtual machines and generate trusted evidence by a group. Subsequently, the cloud server reports the physical platform and VM group's trusted evidence for group verification, reducing latency and improving efficiency. Besides, CloudTA designs a hybrid high concurrency communication framework for supporting remote attestation of large-scale virtual machines by combining active requests and periodic reports. The evaluation results suggest that CloudTA has good efficiency and scalability and can support remote attestation of ten thousand virtual machines.

Novel efficient techniques for real-time cloud security assessment

Cloud provider (CP) leases various resources such as CPUs, memory and storage in the form of virtual machine (VM) instances to clients over internet. This paper tackles the issue of quality of service (QoS) provisioning in cloud environment. We examine using Q-learning for provisioning VMs in the cloud market. The extracted decision function should decide when rejecting new request for VMs that violate QoS guarantee. This problem requires the reward for CP be maximised while simultaneously meeting a quality of service (QoS) constraints. These complex contradicting objectives are embedded in our Q-learning model that is developed and implemented as shown in this paper. Numerical analysis shows the ability of our solution to earn significantly higher revenue than alternatives.

In nowadays cloud computing systems, leveraging the virtualization technology, the customer's requested data computing or storing service is accommodated by a set of mutual-communicated Virtual Machines (VM) in a scalable and elastic manner. These VMs are placed in one or more datacenter nodes according to nodes' capacities, failure probabilities, etc. The VM placement availability refers to the probability that at least one set of the whole customer's requested VMs operates during the entire requested lifetime. The placed VMs should obey the agreed-upon availability, otherwise the cloud provider may face revenue loss. In this paper, we study the problem of placing at most H sets of k requested VMs on minimum number of datacenter nodes, such that the VM placement availability requirement is satisfied and each VM pair has a communication delay no greater than the specified. We prove that this problem is NP-hard. We subsequently propose an exact Integer Nonlinear Program (INLP) and an efficient heuristic to solve this problem. Finally, we conduct simulations to compare the proposed algorithms with two existing heuristics in terms of acceptance ratio, average number of used nodes and running time.

The rapid growth in cloud computing encourages the cloud provider to create more reliable, scalable and efficient cloud environment. For providing such a scalable environment, cloud provider develops large-scale Cloud Data Centers (CDC). Such CDCs consume a large amount of electrical energy for operational purpose and cooling purpose to keep the machines operating at a right temperature, but it also emits a large amount of carbon-dioxide. Thus to decrease the energy consumption, Virtual Machines (VMs) are consolidated to fewer numbers of active servers through VM migration techniques, this leads to Service Level Agreement (SLA) violation and performance degradation. Therefore to retain the energy-performance tradeoff, we should perform an optimum number of VM migrations. There are two cases when the VM migration performed: First when a hot spot is found and Second when a cold spot is found. But in both the cases we do not directly migrate the VM instead first we check whether it is required to migrate the VM. After that, the algorithm finds an appropriate destination host on which the VM will be migrated. In this paper, we implement three approaches to select destination host: First-fit, Best-fit, and Worst-fit. Finally compare the result of a number of VM migration performed, energy consumption and SLA violations with existing one.

Cloud computing is broadly recognized as one of major factors in achieving more flexible, scalable, and efficient systems. However, as customers lose the direct control of their data and applications hosted by cloud providers, the trustworthiness of cloud services is a main issue that hinders the deployment of cloud applications. In this paper, we have developed a novel framework to detect compromises on physical servers in cloud services, via remote attestation with a Trusted Third Party (TTP). Furthermore, to avoid the TTP becoming a bottleneck, we have designed a cloud based TTP platform, using a small private cloud to audit large clouds. We have implemented a prototype system, and evaluated it with several common benchmarks to demonstrate its efficiency. Our experimental results show that the proposed framework is effective in detecting compromise and adds little overhead to a common IaaS cloud environment.

High availability is the most important and challenging problem for cloud providers. However, virtual machine monitor (VMM), a crucial component of the cloud infrastructure, has to be frequently updated and restarted to add security patches and new features, undermining high availability. There are two existing live update methods to improve the cloud availability: kernel live patching and Virtual Machine (VM) live migration. However, they both have serious drawbacks that impair their usefulness in the large cloud infrastructure: kernel live patching cannot handle complex changes (e.g., changes to persistent data structures); and VM live migration may incur unacceptably long delays when migrating millions of VMs in the whole cloud, for example, to deploy urgent security patches. In this paper, we propose a new method, VMM live upgrade, that can promptly upgrade the whole VMM (KVM & QEMU) without interrupting customer VMs. Timely upgrade of the VMM is essential to the cloud because it is both the main attack surface of malicious VMs and the component to integrate new features. We have built a VMM live upgrade system called Orthus. Orthus features three key techniques: dual KVM, VM grafting, and device handover. Together, they enable the cloud provider to load an upgraded KVM instance while the original one is running and cut-and-paste'' the VM to this new instance. In addition, Orthus can seamlessly hand over passthrough devices to the new KVM instance without losing any ongoing (DMA) operations. Our evaluation shows that Orthus can reduce the total migration time and downtime by more than $99%$ and $90%$, respectively. We have deployed Orthus in one of the largest cloud infrastructures for a long time. It has become the most effective and indispensable tool in our daily maintenance of hundreds of thousands of servers and millions of VMs.

With the expansion of the cloud computing usage over a wide range of areas and different kinds of users, the cloud providers are taking full advantage of all their resources as much as they can. Memory is the most expensive resource in terms of oversubscribing and this has resulted in high price to the end user. Furthermore, performing swapping in Virtual Machines (VM) is expensive, so the cloud provider usually do not offer any swapping space for its systems. As a consequence, when a VM runs out of memory, user processes are killed. This scenario in the cloud environment is especially critical, since the user loses all of his/her execution time and, by extension, the money invested in this computation. This paper addresses this critical problem by providing a kernel extension that monitors the memory requirements of a VM and prevents the out of memory state by creating swapping space dynamically. The paper describes the design and implementation of a preliminary prototype of this kernel extensions and evaluates its performance.

Virtual machine placement (VMP) technology is widely used in cloud computing systems. The existing VMP methods mainly aimed at improving the cloud resource utilization, such as load balancing among physical machines (PMs), but they may result in virtual machine (VM) performance degradation because of the great resource contention among VMs running on top of the same PM. In contract to existing VMP algorithms, this paper proposes a virtual machine (VM) Performance maximization and physical machine (PM) Load balancing Virtual Machine Placement method (PLVMP) in cloud, which tries to maximize VM performance and balance PM workload from both users’ and cloud providers’ perspectives. First, we study the relationship between PM workload and VM performance to train a new and improved VM performance model, which can predict VM performance more accurately and offer help to the following VMP. Second, we take VM performance maximization and PM workload balancing into account to formulate the VMP as an optimization problem, which tries to maximize VM performance for users and make load balancing among PMs for cloud providers. Third, we propose a greedy-based algorithm to solve the VMP problem efficiently. We then evaluate PLVMP with other VMP methods on CloudSim platform and a real OpenStack platform. The results show PLVMP can maximize the VM performance significantly and make a good load balancing among PMs.

Cloud computing is an emerging technology in the IT world. Some features of cloud, such as low cost, scalability, robustness and availability are attracting large-scale industries as well as small business towards cloud. A virtual machine (VM) can be defined as a software that can run its own operating systems and applications like an operating system in physical computer. As the number of users increases, allocation of resources and scheduling become a complex task. The optimization of VM provisioning policies offer improvement like increasing provider's profit, energy savings and load balancing in large data centres. In cloud computing when resource requirement of user's requests exceed resources limits of cloud provider, to fulfil the requests the cloud provider outsources to other cloud providers resources, this concept is known as cloud federation. In this paper we propose an algorithm for VM provisioning in federated cloud environment. The approach tries to improve the cloud providers profit. We have used the CloudSim to find-out the results and result show that how Cloud federation help to Cloud providers in order to improve its profit.

CBase: Fast Virtual Machine storage data migration with a new data center structure

Due to the increasing popularity of cloud computing, the cost companies pay to use the cloud and its services are growing significantly. Higher costs of cloud computing technology contribute to the volatility of the cloud and the financial instability of companies that depend on the cloud. Companies have their own unique cloud budget, and staying within that budget can become problematic in light of increasing costs. To save cost, many cloud users look to their cloud provider rather than looking at their own cloud purchases. A lack of studies on cloud users’ virtual machine (VM) workload and how cloud users spend money necessitates analysis of the spending of cloud customers. In this research paper, we analyzed a real-world data set from Microsoft Azure collected in 2019 that includes approximately 2.7 million VM traces. We developed a linear regression based pricing model to calculate the cost and used this model to analyze Microsoft Azure’s VM workload by comparing the cores, memory, lifetime, average utilization, and cost of each trace. By analyzing Microsoft’s data, we observed that users are not fully utilizing the cloud resources they have paid for. With this idea in mind, we then quantified the waste and developed an algorithm to determine which VMs are the most ineffective. We applied our algorithm to Microsoft Azure’s data set, and our results show that our algorithm discovered over one million wasteful VMs and helped 6,600 users save $3.3 million dollars. Even though cloud computing prices are increasing, cloud customers can save significantly by understanding VM workload better and selecting better-fitting VMs.

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.