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Recently Cyber-Physical-Human Systems (CPHS) have been attracted much attention. Unlike existing computing paradigms such as Cloud Computing and Edge Computing, CPHS usually comprise many types of hybrid resources such as software and hardware in the cloud layer, network devices and edge servers in the network layer, as well as the physical devices and human participation. Such hybrid resource makes it challenging to efficient resource scheduling for CPHS. Previous studies on CPHS often focus on improving scheduling efficiency for specific application scenarios and thus are limited for other CPHS. In this work, we present a unified hybrid resource scheduling framework for CPHS, including a hybrid resource model for multiple types of resource providers and participants in CPHS, a hybrid resource orchestration tool that can describe our hybrid resource model as the resource requirements, and a scheduling mechanism to satisfy the resource requirements especially by considering the resource constraints and crosslayer scheduling in CPHS. We perform the case study of typical CPHS scenarios and show that our hybrid resource model and scheduling framework is effective, and provide a new view for improving the resource provision efficiency in CPHS.

Automaton or table-based multi-pattern matching methods have been widely used in cloud services, i.e., virtual Firewall service, virtual IDS service, etc. In cloud, a large scale of patterns in such services are frequently updated causing by users’ joining or quitting and adjustment of security and management policies. Therefore, how to quickly and accurately update the Automaton and Table becomes an important issue. In this paper, we propose Eagle+, an incremental approach for updating the matching Automaton and Table whilst avoiding recalculating the whole patterns after each change. In Eagle+, we attain efficiency by computing only the latest update set of patterns when updating the Automaton and Table. Moreover, Eagle+ achieves accurately local updating based on three atomic operations, adding, updating and deleting, each of which modifies values on classical Aho–Corasick (AC) automaton, Set Backward Oracle Matching (SBOM) automaton and Wu–Manber (WM) table. Compared with existing pattern updating methods, Eagle+ reduces the computation complexity from O ( n 2 ) to O ( n ) . The experimental results show that Eagle+ can save nearly 72%–92% of the time consumption in updating automatons and perform 100X faster in WM table.

The cloud operating system (cloud OS) is used for managing the cloud resources such that they can be used effectively and efficiently. And also it is the duty of cloud OS to provide convenient interface for users and applications. However, these two goals are often conflicting because convenient abstraction usually needs more computing resources. Thus, the cloud OS has its own characteristics of resource management and task scheduling for supporting various kinds of cloud applications. The evolution of cloud OS is in fact driven by these two often conflicting goals and finding the right tradeoff between them makes each phase of the evolution happen. In this paper, we have investigated the ways of cloud OS evolution from three different aspects: enabling technology evolution, OS architecture evolution and cloud ecosystem evolution. We show that finding the appropriate APIs (application programming interfaces) is critical for the next phase of cloud OS evolution. Convenient interfaces need to be provided without scarifying efficiency when APIs are chosen. We present an API-driven cloud OS practice, showing the great capability of APIs for developing a better cloud OS and helping build and run the cloud ecosystem healthily.

Over a decade, cloud and subsequent joint cloud computing has been evolving into one of biggest disruptive technologies in modern digital age. The rapidly maturing cloud service and system management still heavily relies on virtualization which underpins Infrastructure as a Service (IaaS) to offer on-demand and low-cost computing services. Nevertheless datacenters still suffer from low utilization and resource imbalance. IaaS systems and their workloads, as legacy estates, are intricate to be migrated or re-planned, thereby increasing the complexity of utilization improvement. Arguably workload co-location of long-running applications encapsulated in virtual machines and latency-insensitive batch jobs is an alternative to improve overall resource utilization. However, guaranteeing the quality of long-running services is still challenging. In this context, we proposed an isolation-based cluster resource sharing system Shaready to enable workload co-residences. By means of global resource quota configuration and multi-resource isolation, long-running services in virtual machines can be prioritized with maximized resource provisioning. We implemented and validated it based on Openstack and Yarn clusters, and experiments demonstrate that system CPU and memory utilization can be improved by roughly 50% and 16.67% respectively on average with at most 7% performance degradation.

Container technology has been prevalent and widely-adopted in production environment considering the huge benefits to application packing, deploying and management. However, the deployment process is relatively slow by using conventional approaches. In large-scale concurrent deployments, resource contentions on the central image repository would aggravate such situation. In fact, it is observable that the image pulling operation is mainly responsible for the degraded performance. To this end, we propose Cider — a novel deployment system to enable rapid container deployment in a high concurrent and scalable manner at scale. Firstly, on-demand image data loading is proposed by altering the local Docker storage of worker nodes into all-nodes-sharing network storage. Also, the local copy-on-write layer for containers can ensure Cider to achieve the scalability whilst improving the cost-effectiveness during the holistic deployment. Experimental results reveal that Cider can shorten the overall deployment time by 85% and 62% on average when deploying one container and 100 concurrent containers respectively.

Virtual machine live migration technology allows a running VM migrates from one physical host to another with no impact on users. As the scale of distributed computing gets larger and larger, hybrid cloud, which is integrated cloud service utilizing both private and public clouds to perform distinct functions within the same organization becomes a hotspot for both academia and industry. Thus, it will be vital to migrate virtual machines in hybrid clouds. The biggest issue which VM migrationinhybridcloudsfacestodayishowtotransferthelarge amount of storage data. Storage migration of virtual machine with existing mechanisms of memory migration has been studied for a long time. Unfortunately, no one can perform perfectly without modification. It is compelling to consider a newmigration model and optimization methods. In this paper, we analyze the advantage and disadvantage of classical methods, and put forward a method which combines the strengths of pre-copy and post-copy migration model and we also use disk working set to optimize virtual machines storage migration. We have developed and implemented our approach to the QEMU/KVM hypervisor and run a series of experiments. The presented technique shows a reduction of up 20.5% on average of the total transfer time for most of the selected scenario. Our approach can also reduce the migration downtime in most cases.

Software as a Service (SaaS) is a software delivery and business model widely used by Cloud computing. Instead of purchasing and maintaining a software suite permanently, customers only need to lease the software on-demand. The domain of high assurance distributed systems has focused greatly on the areas of fault tolerance and dependability. In a multi-tenant context, it is particularly important to store, manage and provision data services to customers in a highly efficient and dependable manner due to a large number of file operations involved in running such services. It is also desirable to allow a user group to share and cooperate (e.g., co-edit) on some specific data. In this paper we present a dependable data provisioning service in a multitenant Cloud environment. We describe a metadata management approach and leverage multiple replicated metadata caching to shorten the file access time, with the improved efficiency of data sharing. In order to reduce frequent data transmission and data access latency, we introduce a distributed cooperative disk cache mechanism that supports effective cache placement and pull-push cache synchronization. In addition, we use efficient component failover to enhance the service dependability whilst avoiding negative impact from system failures. Our experimental results show that our system can significantly reduce both unused data transmission and response latency. Specifically, over 50% network transmission and operational latency can be saved for random reads while 28.24% network traffic and 25% response latency can be reduced for random write operations. We believe that these findings are demonstrating positive results along the right direction of resolving storage-related challenges in a multitenant Cloud environment.

As the sizes of IT infrastructure continue to grow, cloud computing is a natural extension of virtualisation technologies that enable scalable management of virtual machines over a plethora of physically connected systems. The so-called virtualisation-based cloud computing paradigm offers a practical approach to green IT/clouds, which emphasise the construction and deployment of scalable, energy-efficient network software applications (NetApp) by virtue of improved utilisation of the underlying resources. The latter is typically achieved through increased sharing of hardware and data in a multi-tenant cloud architecture/environment and, as such, accentuates the critical requirement for enhanced security services as an integrated component of the virtual infrastructure management strategy. This paper analyses the key security challenges faced by contemporary green cloud computing environments, and proposes a virtualisation security assurance architecture, CyberGuarder, which is designed to address several key security problems within the 'green' cloud computing context. In particular, CyberGuarder provides three different kinds of services; namely, a virtual machine security service, a virtual network security service and a policy based trust management service. Specifically, the proposed virtual machine security service incorporates a number of new techniques which include (1) a VMM-based integrity measurement approach for NetApp trusted loading, (2) a multi-granularity NetApp isolation mechanism to enable OS user isolation, and (3) a dynamic approach to virtual machine and network isolation for multiple NetApp's based on energy-efficiency and security requirements. Secondly, a virtual network security service has been developed successfully to provide an adaptive virtual security appliance deployment in a NetApp execution environment, whereby traditional security services such as IDS and firewalls can be encapsulated as VM images and deployed over a virtual security network in accordance with the practical configuration of the virtualised infrastructure. Thirdly, a security service providing policy based trust management is proposed to facilitate access control to the resources pool and a trust federation mechanism to support/optimise task privacy and cost requirements across multiple resource pools. Preliminary studies of these services have been carried out on our iVIC platform, with promising results. As part of our ongoing research in large-scale, energy-efficient/green cloud computing, we are currently developing a virtual laboratory for our campus courses using the virtualisation infrastructure of iVIC, which incorporates the important results and experience of CyberGuarder in a practical context.

Virtualization is one of the most fascinating techniques because it can facilitate the infrastructure management and provide isolated execution for running workloads. Despite the benefits gained from virtualization and resource sharing, improved resource utilization is still far from settled due to the dynamic resource requirements and the widely-used over-provision strategy for guaranteed QoS. Additionally, with the emerging demands for big data analytic, how to effectively manage hybrid workloads such as traditional batch task and long-running virtual machine (VM) service needs to be dealt with. In this paper, we propose a system to combine long-running VM service with typical batch workload like MapReduce. The objectives are to improve the holistic cluster utilization through dynamic resource adjustment mechanism for VM without violating other batch workload executions. Furthermore, VM migration is utilized to ensure high availability and avoid potential performance degradation. The experimental results reveal that the dynamically allocated memory is close to the real usage with only 10% estimation margin, and the performance impact on VM and MapReduce jobs are both within 1%. Additionally, at most 50% increment of resource utilization could be achieved. We believe that these findings are in the right direction to solving workload consolidation issues in hybrid computing environments.

Virtualization is one of the main technologies currently used to deploy computing systems due to the high reliability and rapid crash recovery it offers in comparison to physical nodes. These features are mainly achieved by continuously producing snapshots of the status of running virtual machines. In earlier works, the snapshot of each individual VM is performed independently, ignoring the memory similarities between VMs within the cluster. When the size of the virtual cluster becomes larger or snapshots are frequently taken, the size of snapshots can be extremely large, consuming large amount of storage space. In this paper, we introduce an innovative snapshot approach for virtual cluster that exploits shared memory pages among all the component VMs to reduce the size of produced snapshot and mitigate the I/O bottleneck. The duplicate memory pages are effectively discovered and stored only once when the snapshot is taken. In addition, our approach can be not only applied to the stop-copy snapshot but also to the pre-copy mechanism as well. Experiments on both snapshot methods are conducted and the result shows our method can reduce the total memory snapshot files by an average of 30% and reach 63% reduction of the snapshot time compared with the default KVM approach with little overhead of rollback time.

Mobile computing is increasingly growing in terms of massive computation as well as user demand and use of mobile devices. Remote execution techniques enrich the service experience of mobile devices by leveraging the resource pools of computation and storage capabilities of the cloud data center. However, the user experience and quality of service will be severely affected due to the inherent high latency and low bandwidth of a WAN environment. In this paper, we introduce a cloud base station "CloudAP" which is a small-scale computing infrastructure close to mobile users with local network access. In addition, we present a two-tier architecture consisting of CloudAP and Cloud center and show how to synthesize them to form a general computing environment. Furthermore, we propose a prompt execution environment migration scheme implemented by an efficient whole-system VM migration. It makes the execution environment move following the location of mobile device. Our experimental results demonstrate that the proposed architecture is effective and the execution environment migration scheme is efficient, consisting of up to 10ms and 30s for service downtime and execution environment switch time respectively. These improvements make vital contributions to user experience and QoS in mobile pervasive environment.

The combination of cloud computing and virtualization technology introduces a new pattern on resource allocation and utilization. Memory scanning deduplication techniques based on eliminating duplicated pages among virtual machines can promote the resource utilization, and decrease the total cost of ownership. However, the existing memory deduplication technologies lack the supporting of isolation and trustworthiness mechanism. This paper proposes a memory sharing mechanism based on user groups. This mechanism guarantees isolation between the different users on the same host. In addition, we designed a sampling hash algorithm to make the memory scanning process more efficient. We have implemented our approach in Linux by modifying the KSM scanning mechanism and splitting the global ksmd thread into per-group ksmds. The experiment results show the work can optimize the memory-intensive VMs, and efficiently accelerate the memory scanning process.

With the convergence of cloud computing and mobile computing, mobile devices can access remote applications in a cloud environment. However, existing research work mostly focused on leveraging cloud capabilities to enhance mobile clients. Particularly, in order to access different cloud platforms and applications, specific version of clients such as Web portal, Remote Desktop, are generally required. The original display and interaction experience on the client local system are changed. This paper presents an approach named screen which keeps a consistent display and interaction experience between local and remote applications in a mobile cloud computing environment. screen consists of a three-factor merging framework including windows merging, meta-info merging and interaction merging for applications. We developed a prototype of screen for Windows applications to allow thin clients to seamlessly access remote cloud windows applications. Experimental studies show that screen can effectively merge local desktop with remote display, and mobile clients can achieve 20 frames per second when running a remote video playback application. The bandwidth usage of screen is reduced by about 10% compared to Ultra VNC. It performs better especially under high-motion scenarios.

Newly emerging requirements of network application instant service bring in the challenges of a flexible, scalable, and reliable resource environment. Virtualization based resource isolation is helpful. However the internal structure of network software is often neglected in current virtualization based solutions. To improve resource sharing and utilization, the granularity of isolation should evolve from a single host to a network. NeTrOS, a virtual resource operating platform is proposed. Novel core concepts of cyber let and cyber-interrupt are introduced to express the first class runtime managed object and the evolution of virtual computing environment. Key technologies of virtual machine networks (VMNs), the implementation binding of cyber let in NeTrOS, are discussed. A programmable VMN architecture is proposed. Efficient maintenance mechanisms are involved to support the connectivity and dynamic nature of VMNs. Migrations of VMN in both LAN and WAN environment are supported. Detail implementation and deployment of NeTrOS are presented. A series of system evaluation is conducted to show the effectiveness and efficiency of NeTrOS. Experience of a mobile Internet application case supported by NeTrOS is also discussed.

Compared to the conventional physical cluster, the virtual cluster technology has features such as flexible configuration, easy management and high system security, which provides users with an easily customized computing environment. Virtual machine image files as the most important stored objects in the virtual computing environment include all the information about the whole life cycle of virtual machine. The storage of the virtual machine images is an important part of virtual computing. There are mainly two ways to store the images, which include local storage and networking storage. This paper proposes a mechanism to manage the distributed image files to support the virtual computing environment. It uses the computing nodes` local storage as a chunk server in a distributed file system, and regards the distribution of the image files as a primary factor to choose the computing node to deploy virtual machine and to implement a backup mechanism of distributed images based on historical records of image files. Functional test and performance test results show that it can effectively support the virtual computing environment and has great values of applications and research.

Efficient resource management in the virtualized data center is always a practical concern and has attracted significant attention. In particularly, economic allocation mechanism is desired to maximize the revenue for commercial cloud providers. This paper uses overbooking from Revenue Management to avoid resource over-provision according to its runtime demand. We propose an economic model to control the overbooking policy while provide users probability based performance guarantee using risk estimation. To cooperate with overbooking policy, we optimize the VM placement with traffic-aware strategy to satisfy application's QoS requirement. We design GreedySelePod algorithm to achieve traffic localization in order to reduce network bandwidth consumption, especially the network bottleneck bandwidth, thus to accept more requests and increase the revenue in the future. The simulation results show that our approach can greatly improve the request acceptance rate and increase the revenue by up to 87% while with acceptable resource confliction.

Data-Intensive Computing emerges as the fourth paradigm for modern scientific discoveries. MapReduce, a programming paradigm for large-scale data-parallel applications, is widely applied to web indexing, machine learning, and scientific simulations in industries as well as in academia. Recently, the virtualized "utility computing" environments, such as campus cloud, are becoming an important scenario to run MapReduce jobs. For a MapReduce job, the straggler tasks may dominate the response time and delay whole job. Various speculation schemes have been proposed to alleviate such problem, however, most of them implicitly assume that the time cost for data movement on launching speculative map tasks is trivial, which does not always hold for the virtualized Hadoop clusters in a campus cloud. In this paper, we propose a novel approach, CREST(Combination Re-Execution Scheduling Technology), which can achieve the optimal running time for speculative map tasks and decrease the response time of MapReduce jobs. The main idea is that re-executing a combination of tasks on a group of computing nodes may progress faster than directly speculating the straggler task on target node, due to data locality. The evaluation validates our approach and demonstrates that CREST can reduce the running time of a speculative map task by 70% with best cases and 50% on average, comparing with LATE.

In recent years, the Software as a Service, largely enabled by the Internet, has become an innovative software delivery model. During the streaming execution of virtualization software, the execution will wait until the missing data was downloaded, which greatly influences the user experience. In this paper, we present a block-level prefetching framework for streaming delivery of software based on N-Gram prediction model and an incremental data mining algorithm. The prefetching framework uses the historical block access logs for data mining, then dynamically updates and polishes the prefetching rules. The experimental results show that this prefetching framework achieves a launch time reduced by 10% to 50%, as well as hit rate between 81% and 97%.