MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules

Optimizing Virtual Machine I/O Performance in Cloud Environments

Maintaining closeness between data sources and data consumers is crucial for workload I/O performance. In cloud environments, this kind of closeness can be violated by system administrative events and storage architecture barriers. VM migration events are frequent in cloud environments. VM migration changes VM runtime inter-connection or cache contexts, significantly degrading VM I/O performance. Virtualization is the backbone of cloud platforms. I/O virtualization adds additional hops to workload data access path, prolonging I/O latencies. I/O virtualization overheads cap the throughput of high-speed storage devices and imposes high CPU utilizations and energy consumptions to cloud infrastructures. To maintain the closeness between data sources and workloads during VM migration, we propose Clique, an affinity-aware migration scheduling policy, to minimize the aggregate wide area communication traffic during storage migration in virtual cluster contexts. In host-side caching contexts, we propose Successor to recognize warm pages and prefetch them into caches of destination hosts before migration completion. To bypass the I/O virtualization barriers, we propose VIP, an adaptive I/O prefetching framework, which utilizes a virtual I/O front-end buffer for prefetching so as to avoid the on-demand involvement of I/O virtualization stacks and accelerate the I/O response. Analysis on the traffic trace of a virtual cluster containing 68 VMs demonstrates that Clique can reduce inter-cloud traffic by up to 40%. Tests of MPI Reduce_scatter benchmark show that Clique can keep VM performance during migration up to 75% of the non-migration scenario, which is more than 3 times of the Random VM choosing policy. In host-side caching environments, Successor performs better than existing cache warm-up solutions and achieves zero VM-perceived cache warm-up time with low resource costs. At system level, we conducted comprehensive quantitative analysis on I/O virtualization overheads. Our trace replay based simulation demonstrates the effectiveness of VIP for data prefetching with ignorable additional cache resource costs

Elastic Highly Available Cloud Computing

High availability and elasticity are two the cloud computing services technical features. Elasticity is a key feature of cloud computing where provisioning of resources is closely tied to the runtime demand. High availability assure that cloud applications are resilient to failures. Existing cloud solutions focus on providing both features at the level of the virtual resource through virtual machines by managing their restart, addition, and removal as needed. These existing solutions map applications to a specific design, which is not suitable for many applications especially virtualized telecommunication applications that are required to meet carrier grade standards. Carrier grade applications typically rely on the underlying platform to manage their availability by monitoring heartbeats, executing recoveries, and attempting repairs to bring the system back to normal. Migrating such applications to the cloud can be particularly challenging, especially if the elasticity policies target the application only, without considering the underlying platform contributing to its high availability (HA). In this thesis, a Network Function Virtualization (NFV) framework is introduced; the challenges and requirements of its use in mobile networks are discussed. In particular, an architecture for NFV framework entities in the virtual environment is proposed. In order to reduce signaling traffic congestion and achieve better performance, a criterion to bundle multiple functions of virtualized evolved packet-core in a single physical device or a group of adjacent devices is proposed. The analysis shows that the proposed grouping can reduce the network control traffic by 70 percent. Moreover, a comprehensive framework for the elasticity of highly available applications that considers the elastic deployment of the platform and the HA placement of the application’s components is proposed. The approach is applied to an internet protocol multimedia subsystem (IMS) application and demonstrate how, within a matter of seconds, the IMS application can be scaled up while maintaining its HA status

Building Computing-As-A-Service Mobile Cloud System

The last five years have witnessed the proliferation of smart mobile devices, the explosion of various mobile applications and the rapid adoption of cloud computing in business, governmental and educational IT deployment. There is also a growing trends of combining mobile computing and cloud computing as a new popular computing paradigm nowadays. This thesis envisions the future of mobile computing which is primarily affected by following three trends: First, servers in cloud equipped with high speed multi-core technology have been the main stream today. Meanwhile, ARM processor powered servers is growingly became popular recently and the virtualization on ARM systems is also gaining wide ranges of attentions recently. Second, high-speed internet has been pervasive and highly available. Mobile devices are able to connect to cloud anytime and anywhere. Third, cloud computing is reshaping the way of using computing resources. The classic pay/scale-as-you-go model allows hardware resources to be optimally allocated and well-managed. These three trends lend credence to a new mobile computing model with the combination of resource-rich cloud and less powerful mobile devices. In this model, mobile devices run the core virtualization hypervisor with virtualized phone instances, allowing for pervasive access to more powerful, highly-available virtual phone clones in the cloud. The centralized cloud, powered by rich computing and memory recourses, hosts virtual phone clones and repeatedly synchronize the data changes with virtual phone instances running on mobile devices. Users can flexibly isolate different computing environments. In this dissertation, we explored the opportunity of leveraging cloud resources for mobile computing for the purpose of energy saving, performance augmentation as well as secure computing enviroment isolation. We proposed a framework that allows mo- bile users to seamlessly leverage cloud to augment the computing capability of mobile devices and also makes it simpler for application developers to run their smartphone applications in the cloud without tedious application partitioning. This framework was built with virtualization on both server side and mobile devices. It has three building blocks including agile virtual machine deployment, efficient virtual resource management, and seamless mobile augmentation. We presented the design, imple- mentation and evaluation of these three components and demonstrated the feasibility of the proposed mobile cloud model

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Optimized Contract-based Model for Resource Allocation in Federated Geo-distributed Clouds

In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go modelcontinues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scaleand large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises newchallenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resourcestowards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achievingglobal efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies inlocal resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profitearned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPsto establish resource sharing contracts with individual datacentersapriorifor defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model

A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Dynamic Resource Management in Virtualized Data Centres

In the last decade, Cloud Computing has become a disruptive force in the computing landscape, changing the way in which software is designed, deployed and used over the world. Its adoption has been substantial and it is only expected to continue growing. The growth of this new model is supported by the proliferation of large-scale data centres, built for the express purpose of hosting cloud workloads. These data centres rely on systems virtualization to host multiple workloads per physical server, thus increasing their infrastructures\u27 utilization and decreasing their power consumption. However, the owners of the cloud workloads expect their applications\u27 demand to be satisfied at all times, and placing too many workloads in one physical server can risk meeting those service expectations. These and other management goals make the task of managing a cloud-supporting data centre a complex challenge, but one that needs to be addressed. In this work, we address a few of the management challenges associated with dynamic resource management in virtualized data centres. We investigate the application of First Fit heuristics to the Virtual Machine Relocation problem (that is, the problem of migrating VMs away from stressed or overloaded hosts) and the effect that different heuristics have, as reflected in the performance metrics of the data centre. We also investigate how to pursue multiple goals in data centre management and propose a method to achieve precisely that by dynamically switching management strategies at runtime according to data centre state. In order to improve system scalability and decrease network management overhead, we propose architecting the management system as a topology-aware hierarchy of managing elements, which limits the flow of management data across the data centre. Finally, we address the challenge of managing multi-VM applications with placement constraints in data centres, while still trying to achieve high levels of resource utilization and client satisfaction