Optimizing Schedule for Parallel Software Deployments Based on Profiles of Network Activity and Installation Time in Cloud Environments

Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers.

Multiple instances of virtual machines can run on a single physical host sharing hardware and software resources in cloud computing. One of the resources that is shared among multiple Virtual Machines (VM) in the cloud is Cache. Such Virtual machines are targeted for an abnormal activity like side channel attack. Cache-based side channel attack is one of the side channel attack in cloud environment which leaks the private information of the client. The proposed approach includes the detection and mitigation of cache-based side channel attack in cloud infrastructure. The proposed approach comprises of three components: a collection of virtual machine status, the Fuzzy controller to detect attack and mitigation. The fuzzy rule-based controller is incorporated in this approach to identify the cache-attack on the log file. This system works dynamically to prevent cache attacks on the cloud environment and will incur very small overhead in performance.

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.

: The North American Aerospace Defense Command (NORAD) and United States Northern Command (USNORTHCOM) (N-NC) crew training has been hindered by an inability to conduct dynamic training and exercises on multiple Command and Control (C2) systems. There was no common simulation injector because of stove-piped acquisitions and legacy interfaces that were incompatible with the Live-Virtual-Constructive Toolkits. The N-NC Joint Training and Exercise Directorate could not afford traditional replication or emulation of all C2 systems and their data sources, nor the inevitable sustainment costs. This paper presents a cost-effective solution to provide dynamic scenario injection into multiple C2 systems: leveraging server and desktop virtualization technology described in previous I/ITSEC papers. The virtualization process transforms stand-alone systems into functionally equivalent virtual machines (VMs). Server virtualization technology lets multiple VMs run as guests on a single host, and a host can support VMs running different operating systems. This allows entire processing strings, distributed throughout North America, to be converted into VMs on a single server. Because the VMs inherit the fidelity of the actual processors, their outputs are as authentic as the operational systems. These VMs feed processed simulation event data into actual C2 systems or equivalent VMs. Future operational system upgrades can be virtualized and then replace existing VMs without changing this infrastructure. Desktop virtualization technology allows users to run multiple VMs in separate windows on a common display. NNC exploited desktop virtualization to simplify the trainee and model operator's workspaces. They can view and manage multiple VMs with one monitor, keyboard and mouse (controlling simulations, lower echelon processing and operator interaction, and viewing C2 workstation displays).

Cloud computing, the pinnacle of today's technology, has seen a growth tremendously in the past few years due to its demand all over the world. Virtualization is helping cloud computing to rule the world. But, with its growing importance the issues related to faults in virtualization has become the crux of the situation. It is a known fact that cloud computing completely functions on the concept of assigning virtual space to the applications in need. But the whole purpose of it gets beaten when there is any kind of a fault in the virtual machines. This is a vital issue that has to be handled. The proposed technique `load allocation' is the ideology very similar to load balancing feature which works in balancing the load in different virtual machines. This paper aims in using a concept called as Liquid Galaxy, an open source initiative which tries to simulate Google Earth and other software using multiple virtual machines.

As server consolidations based on the virtualization techniques become popular and cloud services continue to grow rapidly, more and more data centers are being built to accommodate a number of virtual clusters running various workloads. Since these virtual clusters often share the resources provided by physical machines (PMs), it is more likely that the interferences between virtual machines (VMs) affect the performance of applications running on top of the virtual clusters. While a lot of studies have proposed different virtual machine placement algorithms to investigate this issue, the problem caused by network performance variability still remains as a challenging issue. Since they usually ignore the CPU overhead to process the communications between VMs, the network bandwidth allocated to a VM cannot be fully utilized when a PM has not enough CPU resources to cover the CPU overhead for VM networking functions. This results in unpredictable application performance running on the virtual clusters. This paper proposes a virtual machine placement algorithm that considers the CPU overhead incurred to reserve network bandwidth in a virtualized data center environment. In order to decide the CPU overhead necessary to guarantee the network bandwidth allocated to a VM, a performance model based on standard linear regression using the data collected from a real environment is used. By comparing the amount of CPU resource available in the driver domain with the CPU overhead obtained from the performance model, the proposed algorithm decides whether the network bandwidth for the VM can be provided or not and selects an appropriate location for VM placement. The benchmarking results show that the proposed algorithm guarantees the network bandwidth allocated to each VM without violations when the CPU resources are shared by multiple VMs.

Nowadays, Cloud Computing acts a major role in every field. These days, more large data centers are in service and many small cloud data centers are enlarging all over the universe. Cloud Computing is a catchword in the domain of HPC and offers on-demand services to the resources on the internet. The VMs (Virtual Machines) specified in the cloud data centres may have different specifications and instable resource usage, which causes imbalanced resource utilization within servers. Thus, it leads to performance degradation. Hence to achieve efficient selection of VM, these challenges must be addressed and solved by using meta-heuristics algorithms. In order to process the data, the VMs are placed on the PMs (Physical Machines). There will be multiple and dynamic request of input in the IaaS(Infrastructure as a Service) framework, hence the system’s responsibility is to create a VMs without knowing the types of tasks. Therefore, the fixed tasks scheduling is not right for this system. The most important research area that needs to be addressed is its performance in scheduling. The best and optimal solution is to find out in the cloud environment. Metaheuristics-based algorithms provide the near-optimal solution. In this paper, we proposed an Improved Particle Swarm Optimization algorithm to reduce the makespan and improve the throughput. We have compared our results with adaptive three-threshold energy-aware (ATEA) algorithm and PSO. The investigational results display the proposed Improved PSO algorithm will schedule and balance the load in the dynamic cloud environment better than the other approaches.

With the tremendous growth in migration to cloud computing for traditional applications, the demand for virtualization is the new need for further research and enhancements. The high demand for virtualization in cloud computing is been addressed with the technology enhancement and in terms of virtual machines. Majorly the hardware component management of virtual machine is considerably reaching the pick of research with the recent advancements by multiple companies and open source researches like AlphaVM, Hyper-V, Integrity Virtual Machines, JPC (Virtual Machine), PowerVM, Sun xVM, VMware Workstation and z LPARs. Another most important component of virtualization and virtual machine image storage is been the bottleneck for the further advancement. The limitations are been addressed by clustering and storage area networking, however these existing solutions are no match for the industrial grade demand for the VM image storage requirements.Hence in this work we propose a cloud based virtual machine storage framework designed for large scale deployment satisfying the other needs for storage features like replication and measurements of performance improvements. This work also demonstrates the advancements in performance for virtualization management

This paper addresses the problem of simultaneously migrating a group of co-located and virtual machines (VMs), i.e, VMs executing on the same physical machine. We refer to such a mass simultaneous migration of active VMs as live gang migration. Cluster administrators may often need to perform gang migration for load balancing, system maintenance, or power savings. Application performance requirements may dictate that the total migration time, network traffic overhead, and service downtime, be kept minimal when migrating multiple VMs. State-of-the-art migration techniques optimize the migration of a single VM. In this paper, we optimize the simultaneous migration of multiple co-located VMs. We present the design, implementation, and evaluation of a de-duplication based approach to perform concurrent migration of co-located VMs. Our approach transmits memory content that is identical across VMs only once during migration to significantly reduce both the total migration time and network traffic. Using the QEMU/KVM platform, we detail a proof-of-concept prototype implementation of two types of de-duplication strategies (at page level and sub-page level) and a differential compression approach to exploit content similarity across VMs. Evaluations over Gigabit Ethernet with various types of VM workloads demonstrate that our prototype for gang migration can achieve significant reductions in both network traffic and total migration time.

Cloud computing has become the backbone of modern digital infrastructure, driving everything from enterprise business applications to consumer services.But strong demand for cloud services has driven up energy consumption, spurring operational costs and threatening environmental sustainability.When it comes to energy efficiency, virtual machines or VMs, the most important and fundamental unit of resource management in cloud environments, hold enormous opportunities but also pitfalls.This study study evaluates the contributions of Artificial intelligence (AI) on these areas for the efficient utilisation of energy in cloud data centres through Virtual Machines (VMs).The use of AI like machine learning, deep learning, and reinforcement learning enables components like dynamic resource allocation, load prediction, and intelligent load balancing that helps minimize the used energy while maintaining reliability.Artificial intelligence based approaches such as energyaware Virtual Machine placement, predictive management of busy servers or real-time tracking of energy consumption can be applied to overcome energy inefficiencies.An additional boost in greener operations can come from integrating renewable sources into cloud systems through the help of AI.Examples of AI applications responsible for lowering energy expenses and increasing efficiency come from SOURCE companies such as Google and Amazon.Many future trends are addressed in the paper, including the autonomous management of AI driven Data centers and Smart Energy powered by IoT, along with challenges that EI faces while implementing AI for energy optimization.Human-level sentence: It highlights the game-changing importance of Artificial Intelligence in reaching sustainable, green cloud computing environments focused more on the energy savings of virtual machines.

Presently maintaining the load is the most challenging issue for a researcher in a cloud environment. In the IT sector, cloud computing is offering a variety of resources and free resources based on robust technology and Internet access in cloud computing. Users can also keep doing, gives a very reliable and inexpensive. Therefore, users of cloud computing are now evolving for every department of the organization, government and education for increased use of cloud services. Then, when they come in a variety of user demand for cloud resources, we can use load balancing technology to meet user needs. The use of the load balancing process is useful for distributing load over load to another node to adjust. The aim of load balancing is to balance virtual machines that should not be under load or overload. In this paper, propose two algorithms for the balance of load balancing algorithm including the workload of cloud system. In our work, there are basically low load systems and virtual machine demand will increase. The minimum finish time would be to put everything on one virtual machine and see the lowest number of high priority virtual machines. Improvements will be made to the importance of our research system, better use of resources and reduced full-time. Propose paper want the ClaudSim cloud platform to use a simulator. Also use unit response time in a VM as a metric, full implementation, and operation. Events demonstrate the features of the proposed algorithm running in a virtual machine to be used on experimental scale and for better application performance as possible.

In elastic cloud computing environment, multiple virtual machines may reside in the same physical machine for services consolidation. For the same residential guest domains or multi-tiered hosting services, the inter-domain communications are complex and frequent. However, traditional inter-domain communications are conducted through the virtual network interfaces of both sending and receiving virtual machines. Such TCP/IP networking communications involve lengthy communication path and frequent kernel interactions and thus downgrade the I/O performance of virtual machines. In this paper, we present an optimized inter-domain communication approach based on shared memory to improve the communication performance of multiple virtual machines residing in the same physical machine. In our approach, the sending data is shared in memory, not transferred via TCP/IP stack. The communication data are directly mapped into user space of the virtual machine and therefore it reduces useless system calls and context switches. The memory is shared via a customized shared device kernel module with bidirectional event channel resided in both communicating virtual machines. We use state flags in circular buffer to reduce wait and notify operations and thus reduce the system calls. Experiments show that our proposed approach can provide 5 time higher throughput and 2.5 times less latency compared to TCP/IP communication path.

Over the last few years, cloud computing has emerged as the best option for offering various applications. It can supply databases, web services, processing, storage, development platforms, and web services to help businesses swiftly expand their infrastructure and service offerings. However, massive amounts of data will severely burden the cloud computing environment. Due to this, load‐balanced task scheduling has remained a crucial aspect of resource distribution from a data center, ensuring that each virtual machine (VM) has a balanced load to fulfill its full potential. Overloading or underloading a host or server can cause issues with processing speed or even cause a system crash. To prevent this, we need an intelligent way to schedule tasks. Therefore, the hybrid optimization algorithm called gazelle coati optimization algorithm (GCOA) is introduced in this paper to schedule tasks in a cloud environment. This algorithm integrates the coati optimization algorithm (COA) and the gazelle optimization algorithm (GOA) to enhance the GOA's exploitation process. The main objective of this hybrid approach is to optimize scheduling, maximize VM throughput and resource utilization, and establish load balancing between VMs based on makespan, energy, and cost. The performance assessment of the proposed approach is conducted on two real‐world workloads, such as Google Cloud Jobs (GoCJ) and the heterogeneous computing scheduling problems (HCSP) datasets, using several performance metrics, and the results are compared with the previous scheduling and load balancing methods. The experiment results show that the suggested strategy produced significant gains in makespan, energy, cost, resource utilization, and throughput—up to 10% and 60%, respectively—making it appropriate for real‐world cloud infrastructures.

With the application of cloud computing services in more and more fields, it will undertake more computing tasks and storage tasks. The problem of high energy consumption in data centers will become more serious. Virtualization technology is very important in cloud computing, which can improve the utilization rate of resources. At the same time, it has flexibility in resource scheduling and can integrate multiple virtual machines to achieve power efficiency. Using online virtual machine migration technology for energy-saving planning in cloud environment is a hot research topic in academic circles. The scheduling strategy proposed in this paper can reduce the server downtime and the number of server hosts, so as to achieve the maximum use of resources. The main research work of this paper includes the following aspects: Firstly, the energy consumption in the process of virtual machine energy-saving design is modeled, and the relationship between energy consumption and resource usage under different load conditions is analyzed, and the problems are abstracted, e.g., packaging box problem. Secondly, this paper uses genetic algorithm to solve the problem of high energy consumption. Finally, based on the target allocation scheme of virtual machines obtained by the above method, the migration problem of virtual machines is abstracted as the problem of finding the maximum weighted independent set of graphs. And a greedy algorithm is designed to solve this problem. In this paper, CloudSim simulation platform is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed algorithm can reduce data energy consumption and avoid frequent migration of virtual machines.

With the rapid development of the Web, cloud computing has received much attention. The key enabling factor for cloud computing is the virtualization technology by which multiple virtual machines can run in a shared physical machine and enable high utilization of hardware resources. However, the consolidation also introduces contention in shared resources, leading to degraded application performance. Then, one of challenges is to detect and mitigate the performance crisis where one or more virtualized machines’ performance target is not achieved. We present a method for mitigating the performance crisis, which leverages the performance interference model to predict the possibility of the performance crisis caused by the consolidation and employs this model to learn the bad or good consolidation plan to identify a new placement strategy to mitigate the performance crisis. Our experiments show the better performance of the mitigating methods.