AMAD: Resource Consumption Profile-Aware Attack Detection in IaaS Cloud

Cloud computing provides a computing platform for the users to meet their demands in an efficient way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines are utilized to satisfy the user needs and are placed on physical machines of the cloud for effective usage of hardware resources and electricity. Optimizing the number of physical machines used helps in cutting down the power consumption by substantial amount. An optimal technique is to map virtual machines to physical machines such that the number of required physical machines is minimized. The Virtual Machine Placement problem with the target of minimizing the total energy consumption by running physical machines is an indication of increasing resource utilization and reducing cost of a data center. Virtual Machine Migration is another approach to minimize the total energy consumption and to increase resource utilization. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. Migration support can balance the utilization of multi-dimensional resources, reduce the number of running physical machines and thus lower the energy consumption. In this paper, we present a platform to test different Virtual Machine Placement and Virtual Machine Migration algorithms on real time scenarios (Cloud) and enable them to conduct different experiments. For this purpose, OpenStack private cloud is suggested as private cloud environment and OpenStack Neat is used to provide dynamic Virtual Machine Migration. Here, we have integrated OpenStack Neat with OpenStack Cloud and shown the experiment of dynamic Virtual Machine Migration in real time.

In cloud computing, frequent addition and removal of virtual machines (VMs) can cause the resource waste or load imbalance among physical machines (PMs). Replacement of VMs to appropriate PMs needs to be performed periodically, which is called VM migration (VMM). The exist VMM methods mainly aim at optimizing resource utilization for cloud providers, but they neglect the VM performance, which may affect user experience. In contract to existing VMM methods, this paper proposes a Performance-Guaranteed Virtual Machine Migration Strategy (PGVMM) from users' perspectives, which tries to guarantee VM performance for users. First, we study the relationship between the PM workload and the VM performance to improve our previous VM performance model, which can offer help to the following VMM. Second, we take the VM performance and migration cost into account to formulate the VMM as an optimization problem, which tries to guarantee VMs performance with the least migration cost. Third, we propose a dual-threshold greedy-based algorithm to solve the VMM problem efficiently. Then some experiments are conducted to evaluate our algorithm. The results also prove the efficiency of the proposed method.

Cloud data centers consume an enormous amount of energy. Virtual Machine (VM) migration technology can be applied to reduce energy consumption by consolidating VMs onto the minimal number of servers and turn idle servers into power-saving modes. While most existing energy models consider mainly computing energy, an enhanced energy consumption model is formulated, which includes energy consumption for computation, for servers to switch from standby to active modes, and for communication during VM migrations. Next, two new dynamic VM migration algorithms are proposed. They apply a local regression method to predict potentially over-utilized servers, and the 0-1 knapsack dynamic programming to find the best-fit combination of VMs for migration. The time complexity of these algorithms is analyzed, which indicates that they are highly scalable. Performance is evaluated and compared with existing algorithms. The two new heuristics have significantly reduced the number of VM migration, the number of rebooted servers, and energy consumption. Furthermore, one of them has achieved the least overall SLA violations. We believe that the new energy formulation and the two new heuristics contribute significantly towards achieving green cloud computing.

Live Virtual Machine (VM) migration aims to move a VM between hosts without interruption to the services running in the VM. It is a cornerstone technology for cloud management and is critical for decreasing the operating cost of cloud providers and improving service quality. Many efforts have been made to improve the performance of live VM migration and a variety of achievements have been gained. But some key problems still require solutions or improvements. In addition, with the development and evolution of cloud computing, more and more applications are running in a data center, and some new cloud computing paradigms (e.g., Mobile Edge Computing (MEC)) are being developed as well. This brings more new problems and meanwhile new optimization opportunities for live VM migration. The goal of this dissertation is to identify and qualify the performance bottleneck, and then optimizes the performance of live VM migration in different deployment scenarios. Firstly, a comprehensive review of VM migration and optimization technologies is conducted. Then, according to the lessons learned from the review, a series of optimizations are proposed to solve different problems with live VM migration. For the migration in a Local Area Network (LAN) or within a data center, we build corresponding performance models, and design a new performance control algorithm which takes users’ requirements into consideration. This algorithm also solves the convergence problem with pre-copy migration. For the migration over a Wide Area Network (WAN) or across data centers, several mechanisms are proposed to improve the migration performance of VM storage data which is the bottleneck for across-data-center VM migration. Specifically, a three-layer VM image structure and a central base image repository are designed to increase data sharing between VMs and data centers. Based on these two structures, several optimization strategies are proposed for data deduplication and Peer-to-Peer (P2P) file sharing to further improve VM storage data migration performance. For the migration in MEC, several algorithms are designed to mitigate the problems with user mobility-induced VM migration. According to the presented migration environments, user mobility is divided into two categories. Based on this classification, two algorithms are designed to improve migration performance, two mechanisms are designed to reduce the service degradation resulting from VM migration and two algorithms are designed to lower the network overhead of constant VM migration due to user mobility.

The virtual machine have memory bottleneck in the application, two mainly aspects are the I/O limit and dynamic virtual machine migtation.GlusterFS is a distributed file storage system with high level and open source uses the Scale-Out architecture and elastic hash algorithm to solve the I/O limit bottleneck.GlusterFS can automatically copy files and provides file sharing service to solve the virtual machine dynamic migration bottleneck.In order to use GlusterFS as the underlying storage devices in a cloud environment, the iozone file system is adopted to test the performance of GlusterFS.The results show that the storage performance of GlusterFS can be improved linear through increasing dynamically the number of physical servers, the speed is stable when multi clients write large files to GlusterFS at the same time, users can define his own data backup number.Therefore, it is a good choice to use GlusterFS to solve the storage bottleneck on virtual machines in a cloud environment.

Data centres are networking platforms which exhibit virtual machine workload execution in a dynamic manner. As the users’ requests are of enormous magnitude, it manifests as overloaded physical machines resulting in quality of service degradation and SLA violations. This challenge can be negotiated by exercising a better virtual machine allocation by dint of re-allocating a subset of active virtual machines at a suitable destined server by virtual machine migration. It is exhibited as improved resource utilization with enhanced energy efficiency along with addressing the challenge of impending server overloading resulting in downgraded services. The aforesaid twin factors of enhanced energy consumption and enhanced resource utilization can be suitably addressed by combining them together as a single objective function by utilizing cost function based best-fit decreasing heuristic. It enhances the potentials for aggressively migrating large capacity applications like image processing, speech recognition, and decision support systems. It facilitates a seamless and transparent live virtual machine migration from one physical server to another along with taking care of cloud environment resources. The identification of most appropriate migration target host is executed by applying modified version of best-fit decreasing algorithm with respect to virtual machine dynamic migration scheduling model. By executing the selection algorithm, the hotspot hosts in cloud platform are segregated. Subsequently, virtual machine-related resource loads are identified in descending order with respect to hotspots. The resource loads pertaining to non-hotspot hosts are identified in ascending order. Next, the traversing manoeuvring in non-hotspot hosts queue is exercised for identification of the most appropriate host to be reckoned as migration target host.KeywordsData centreLoad balanceVirtual machine migrationCloud computing

EnTruVe: ENergy and TRUst-aware Virtual Machine allocation in VEhicle fog computing for catering applications in 5G

Virtual machines are widely used to support applications with virtual service in server clusters. Here, a virtual machine can migrate from a host server to a guest server. In this paper, we consider a cluster where virtual machines are dynamically created and dropped depending on the number of processes. We propose a dynamic virtual machine migration (DVMM) algorithm to reduce the total electric energy consumption of servers. If an application issues a process to a cluster, the most energy-efficient host server is first selected and then the process is performed on a virtual machine of the server. Then, a virtual machine migrates from a host server to a guest server so that total electric energy consumption of servers can be reduced. In the evaluation, we show the total electric energy consumption and active time of servers and the average execution time of processes can be reduced in the DVMM algorithm.

In the evolution toward fifth‐generation networks, Mobile Edge Computing (MEC) is an emerging paradigm, conceived to meet the ever‐increasing computational demands of mobile applications. Within the access range of mobile devices, the MEC technique promises the enablement of efficient Mobile Cloud Computing services. In an MEC system, virtual machine (VM) migration is a key issue; VM migration is the process of moving a VM from an edge node to another edge node. To improve service quality and system performance, the VM migration method has a dual focus on the MEC system's computation and communication resources. In this study, we formulate the VM migration problem as a one‐on‐one contract game model and develop a learning‐based price control mechanism to effectively handle the MEC's resource. By using the game methodology and learning process, our approach is able to capture the dynamics of MEC systems, and it interacts continually with an unknown system environment. Finally, extensive simulation results are provided to demonstrate the capability of the proposed approach in achieving, with respect to existing MEC schemes, both higher resource utilization and system throughput, as well as reduced service drop ratio and reduced service delay.

Virtualization technology plays an important role in cloud computing. Virtual machine (VM) migration not only enables load balancing of hosts in data center to avoid overload anomalies, but also reduces the cost of cloud computing data centers. Our work mainly focused on the communication costs of VMs migration in data center. In this paper, a double auction-based VM migration algorithm is proposed, which takes the cost of communication between VMs into account under normal operation situation. The algorithm of VM migration is divided into two parts: (i) selecting the VMs to be migrated according to the communication and occupied resources factors of VMs and (ii) determining the destination host for VMs which to be migrated. In the first process of VM migration, we proposed VMs greedy selection algorithm (VMs-GSA) to select VMs. A VM Migration Double Auction Mechanism was applied to the second process of VM migration to obtain the mappings between VMs and underutilized hosts. The simulation result shows that the proposed VM migration algorithm-based heuristic is efficient. The traffic generated by VMs-GSA is 35% less than the random algorithm, and the success rate of VM migration is very high.

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks (DCNs). Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. When the VM demands change, however, a great cost will be paid on the VM migration. With the advance of software-defined network (SDN), recent studies have shown great potential to implement an adaptive network topology at a low cost. Taking advantage of the topology adaptability, in this paper, we propose a new paradigm for VM migration by dynamically constructing a topology based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. Then we develop a novel progressive-decompose-rounding (PDR) algorithm to solve this problem in polynomial time with a proved approximation ratio. Extensive trace-based simulations show that PDR can achieve higher flow throughput among VMs with only a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solution.

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks. Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. As the flow demands between VMs change, however, a great cost will be paid for the VM migration. In this paper, we propose a new paradigm for VM migration by dynamically constructing adaptive topologies based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. For periodic traffic, we develop a novel progressive-decompose-rounding algorithm to schedule VM migration in polynomial time with a proved approximation ratio. For highly dynamic flows, we design an online decision-maker (ODM) algorithm with proved performance bound. Extensive trace-based simulations show that PDR and ODM can achieve about four times flow throughput among VMs with less than a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solutions.

Live migration of virtual machine (VM) enables mobility of VM and contributes to advantages of virtualization like energy saving, high availability, fault tolerance and work load balancing. However solutions of VMs' migration in both theoretical and industrial areas concentrate more on memory migration other than storage migration. Lots of applications with intensive disk I/O operations rely on local storage, especially when it comes to high performance computing. Migration of shared storage is also of necessity for consolidation and workload balance. Current approaches on storage migration can hardly work effectively in disk I/O intensive environment. They cannot reduce migration time and guarantee the disk I/O performance of VMs at the same time. This paper proposes an approach called Partners Assisted Storage Migration (PASM). We are the first to utilize disk I/O ability of pre-allocated storage nodes to relieve the competition between VMs' intensive disk I/O and storage migration. It can migrate VMs' storage effectively comparing to current methods: post-copy and write-mirror. Experiments including single VM's migration and multiple VMs' migration show that PASM can save 78.9% migration time and achieve additional 27.1% in disk I/O performance over existing methods.

Virtualization technology plays an important role in cloud computing. Virtual machine (VM) migration can reduce the cost of cloud computing data centers. In this paper, a double auction-based VM migration algorithm is proposed, which takes the cost of communication between VMs into account under normal operation situation. The algorithm of VM migration is divided into two parts: (1) selecting the VMs to be migrated according to the communication and occupied resources factors of VMs, (2) determining the destination host for VMs which to be migrated. We proposed VMs greedy selection algorithm (VMs-GSA) and VM migration double auction mechanism (VMM-DAM) to select VMs and obtain the mappings between VMs and underutilized hosts. Compared with other existing works, the algorithms we proposed have advantages.

In the era of cloud computing, virtualization based fault tolerance that utilizes the continuous virtual machine (VM) migration to synchronize a VM and its remote replica is a common technique to achieve high availability. However, traditional live VM migration, whose goal is to minimize the system downtime, has a long duration owing to the expense of the pre-copy for machine status and memory content, which increases the period of failover when failures occur. In this paper, we proposed a new VM migration method, called Fast VM Migration (FVMM), which utilizes the templating technique to accelerate the VM migration by reducing the cost of pre-copy. The templating technique that creates VMs from a master copy, called a template, is a usually used to deploy many similar VMs in a large virtual environment. FVMM employs VM templating to mitigate the cost of pre-copy. Its implementation is optimized with six new techniques: SFVMM and LFVMM, COW templating, continuous templating, asynchronous transmission buffering, VM recovery from continuous templating, and templating regularly. We also applied FVMM for virtualization based fault tolerance to accelerate the failover process. Experimental results show that FVMM is more than 56 times faster than pre-copy live migration for a VM with 16GB, and can reduce upto 32% of the system downtime and the fault tolerance resynchronization time comparing to the methods using pre-copy live migration.