Johan Tordsson

Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.

Reservoir - When One Cloud Is Not Enough

As cloud computing becomes more predominant, the problem of scalability has become critical for cloud computing providers. The cloud paradigm is attractive because it offers a dramatic reduction in capital and operation expenses for consumers.

An adaptive hybrid elasticity controller for cloud infrastructures

Cloud elasticity is the ability of the cloud infrastructure to rapidly change the amount of resources allocated to a service in order to meet the actual varying demands on the service while enforcing SLAs. In this paper, we focus on horizontal elasticity, the ability of the infrastructure to add or remove virtual machines allocated to a service deployed in the cloud. We model a cloud service using queuing theory. Using that model we build two adaptive proactive controllers that estimate the future load on a service. We explore the different possible scenarios for deploying a proactive elasticity controller coupled with a reactive elasticity controller in the cloud. Using simulation with workload traces from the FIFA world-cup web servers, we show that a hybrid controller that incorporates a reactive controller for scale up coupled with our proactive controllers for scale down decisions reduces SLA violations by a factor of 2 to 10 compared to a regression based controller or a completely reactive controller.

Evaluation of delta compression techniques for efficient live migration of large virtual machines

Despite the widespread support for live migration of Virtual Machines (VMs) in current hypervisors, these have significant shortcomings when it comes to migration of certain types of VMs. More specifically, with existing algorithms, there is a high risk of service interruption when migrating VMs with high workloads and/or over low-bandwidth networks. In these cases, VM memory pages are dirtied faster than they can be transferred over the network, which leads to extended migration downtime. In this contribution, we study the application of delta compression during the transfer of memory pages in order to increase migration throughput and thus reduce downtime. The delta compression live migration algorithm is implemented as a modification to the KVM hypervisor. Its performance is evaluated by migrating VMs running different type of workloads and the evaluation demonstrates a significant decrease in migration downtime in all test cases. In a benchmark scenario the downtime is reduced by a factor of 100. In another scenario a streaming video server is live migrated with no perceivable downtime to the clients while the picture is frozen for eight seconds using standard approaches. Finally, in an enterprise application scenario, the delta compression algorithm successfully live migrates a very large system that fails after migration using the standard algorithm. Finally, we discuss some general effects of delta compression on live migration and analyze when it is beneficial to use this technique.

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.

Towards Secure Cloud Bursting, Brokerage and Aggregation

The cloud based delivery model for IT resources is revolutionizing the IT industry. Despite the marketing hype around “the cloud”, the paradigm itself is in a critical transition state from the laboratories to mass market. Many technical and business aspects of cloud computing need to mature before it is widely adopted for corporate use. For example, the inability to seamlessly burst between internal cloud and external cloud platforms, termed cloud bursting, is a significant shortcoming of current cloud solutions. Furthermore, the absence of a capability that would allow to broker between multiple cloud providers or to aggregate them into a composite service inhibits the free and open competition that would help the market mature. This paper describes the concepts of cloud bursting and cloud brokerage and discusses the open management and security issues associated with the two models. It also presents a possible architectural framework capable of powering the brokerage based cloud services that is currently being developed in the scope of OPTIMIS, an EU FP7 project.

A grid resource broker supporting advance reservations and benchmark-based resource selection

This contribution presents algorithms, methods, and software for a Grid resource manager, responsible for resource brokering and scheduling in early production Grids. The broker selects computing resources based on actual job requirements and a number of criteria identifying the available resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, input/output data transfer, and executable staging. Main features of the resource manager include advance reservations, resource selection based on computer benchmark results and network performance predictions, and a basic adaptation facility.

An interoperable, standards-based grid resource broker and job submission service

We present the architecture and implementation of a grid resource broker and job submission service, designed to be as independent as possible of the grid middleware used on the resources. The overall architecture comprises seven general components and a few conversion and integration points where all middleware-specific issues are handled. The implementation is based on state-of-the-art grid and Web services technology as well as existing and emerging standards (WSRF, JSDL, GLUE, WS-Agreement). Features provided by the service include advance reservations and a resource selection process based on a priori estimations of the total time to delivery for the application, including a benchmark-based prediction of the execution time. The general service implementation is based on the Globus Toolkit 4. For test and evaluation, plugins and format converters are provided for use with the NorduGrid ARC middleware

Improving cloud infrastructure utilization through overbooking

Despite the potential given by the combination of multi-tenancy and virtualization, resource utilization in todays data centers is still low. We identify three key characteristics of cloud services and infrastructure as-a-service management practices: burstiness in service workloads, fluctuations in virtual machine resource usage over time, and virtual machines being limited to pre-defined sizes only. Based on these characteristics, we propose scheduling and admission control algorithms that incorporate resource overbooking to improve utilization. A combination of modeling, monitoring, and prediction techniques is used to avoid overpassing the total infrastructure capacity. A performance evaluation using a mixture of workload traces demonstrates the potential for significant improvements in resource utilization while still avoiding overpassing the total capacity.

Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control

Elasticity is the ability of a cloud infrastructure to dynamically change the amount of resources allocated to a running service as load changes. We build an autonomous elasticity controller that changes the number of virtual machines allocated to a service based on both monitored load changes and predictions of future load. The cloud infrastructure is modeled as a G/G/N queue. This model is used to construct a hybrid reactive-adaptive controller that quickly reacts to sudden load changes, prevents premature release of resources, takes into account the heterogeneity of the workload, and avoids oscillations. Using simulations with Web and cluster workload traces, we show that our proposed controller lowers the number of delayed requests by a factor of 70 for the Web traces and 3 for the cluster traces when compared to a reactive controller. Our controller also decreases the average number of queued requests by a factor of 3 for both traces, and reduces oscillations by a factor of 7 for the Web traces and 3 for the cluster traces. This comes at the expense of between 20% and 30% over-provisioning, as compared to a few percent for the reactive controller.