Heterogeneous IaaS Research Articles

CoLocateMe: Aggregation-Based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 9.61% more energy and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs.

Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

Cloud Service Providers are speedily becoming the target platform for scientific workflow computations due to the massive possible and flexible pay-as-you-go pricing model. Workflow allocation problem in cloud systems is considered NP-hard. A heterogeneous IaaS cloud could be fully effective if the allocation method provides an efficient mapping between virtual machines (VMs) and workflow applications demanding execution. First, we model multiple workflow allocation problem in the cloud environment. Then, we propose a levelized multiple workflow allocation strategy with task merging (LMWS-TM) to optimize turnaround time for multiple workflow applications in the Infrastructure as a Service (IaaS) cloud environment to achieve better performance. The task merging scheme is incorporated into workflows after partitioning and prior to allocation to reduce inter-task communication share and the total number of depth levels for improving the overall completion time. Moreover, it considers the inter-task communication and inter-machine distance for estimating communication cost share among tasks on the schedule generated. Furthermore, the scheme is capable enough to use simple and flexible level attributes to tackle precedence constraints. Afterward, we conducted an experimental study to evaluate LMWS-TM by comparative performance analysis with its peers, namely SLBBS, DLS, and HEFT, on quality of service (QoS) parameters, namely, turnaround time, system utilization, flow time, and response time. The study reveals the superior performance of LMWS-TM among its considered peers in almost all the cases for almost all considered parameters under investigation. Finally, we performed statistical testing to test the significance level using SPSS 20, confirming the hypothesis drawn in the experimental study.

Quantitative Fault-Tolerance for Reliable Workflows on Heterogeneous IaaS Clouds

Reliability requirement is one of the most important quality of services (QoS) and should be satisfied for a reliable workflow in cloud computing. Primary-backup replication is an important software fault-tolerant technique used to satisfy reliability requirement. Recent works studied quantitative fault-tolerant scheduling to reduce execution cost by minimizing the number of replicas while satisfying the reliability requirement of a workflow on heterogeneous infrastructure as a service (IaaS) clouds. However, a minimum number of replicas does not necessarily lead to the minimum execution cost and shortest schedule length in a heterogeneous IaaS cloud. In this study, we propose the quantitative fault-tolerant scheduling algorithms QFEC and QFEC+ with minimum execution costs and QFSL and QFSL+ with shortest schedule lengths while satisfing the reliability requirements of workflows. Extensive experimental results show that (1) compared with the state-of-the-art algorithms, the proposed algorithms achieve less execution cost and shorter schedule length, although the number of replicas are not minimum; (2) QFEC and QFEC+ are designed to reduce execution cost, and QFEC+ is better than QFEC for all low-parallelism and high-parallelism workflows; and (3) QFSL and QFSL+ are designed to decrease schedule length, and QFSL+ is better than QFSL for all low-parallelism and high-parallelism workflows.

GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.

Context Aware VM Placement Optimization Technique for Heterogeneous IaaS Cloud

Ever increasing demand for cloud adoption is prompting researchers and engineers around the world to make cloud computing more efficient and beneficial for cloud service providers and users. Cloud computing brings profits for all when the cloud infrastructure is used efficiently, and its services are made affordable to businesses of all scales. Managing cloud data center incurs a significant cost, which includes investing in IT infrastructure at the beginning and data center management costs for power, repair, space, and so on at later stages. The power costs are contributing to a significant share in overall data center management costs, and saving in power consumption can help reduce management costs for data center owners. This paper proposes an efficient context-aware adaptive heuristic-based solution for the virtual machine (VM) placement optimization in the heterogeneous cloud data centers. The proposed VM placement technique takes into the account of physical machine characteristics and load (peak and non-peak) conditions in the heterogeneous data centers to save power and also improve performance efficiency for data center owners. The experiments conducted with real cloud workloads and also synthetic workloads against a well-known adaptive heuristic-based technique indicate significant performance improvements and energy saving with our proposed solution.

Optimum Application Deployment Technology for Heterogeneous IaaS Cloud

Optimum Application Deployment Technology for Heterogeneous IaaS Cloud

Standards for enabling heterogeneous IaaS cloud federations

Technology market is continuing a rapid growth phase where different resource providers and Cloud Management Frameworks are positioning to provide ad-hoc solutions–as management interfaces, information discovery or billing–trying to differentiate from competitors resulting in incompatibilites between them when addressing more complex scenarios like federated clouds.Therefore, grasping interoperability problems present in current infrastructures by studying how existing and emerging standards could enhance the cloud user experience.In this paper we will review the current open challenges in Infrastructure as a Service cloud interoperability and federation, as well as point to the potential standards that should alleviate these problems.

Enforcing CPU allocation in a heterogeneous IaaS

In an Infrastructure as a Service (IaaS), the amount of resources allocated to a virtual machine (VM) at creation time may be expressed with relative values (relative to the hardware, i.e., a fraction of the capacity of a device) or absolute values (i.e., a performance metric which is independent from the capacity of the hardware). Surprisingly, disk or network resource allocations are expressed with absolute values (bandwidth), but CPU resource allocations are expressed with relative values (a percentage of a processor). The major problem with CPU relative value allocations is that it depends on the capacity of the CPU, which may vary due to different factors (server heterogeneity in a cluster, Dynamic Voltage Frequency Scaling (DVFS)). In this paper, we analyze the side effects and drawbacks of relative allocations. We claim that CPU allocation should be expressed with absolute values. We propose such a CPU resource management system and we demonstrate and evaluate its benefits.

Agents-based deployment of heterogeneous IaaS clouds

Cloud technology has provided much more effective and flexible management of computing infrastructure both to service vendors and customers. However, the provisioning of computing resources and deployment of applications using heterogeneous infrastructure as a service IaaS clouds is not straight forward, because offers are not easily comparable, providers ask for using different technologies and a standard is still lacking. Moreover cloud providers often hinder the adoption of a common standard in order to lock customers to their solutions. To address these issues we have designed and developed, within the research activities of the mOSAIC project, an agents-based solution that provides a uniform interface to IaaS provisioning and management. The Cloud Agency uses a set of vendor agents implementing adapters to different commercial cloud providers and open source cloud technologies. Starting from the computational constraints and from the application requirements, the vendor agents allow the users to negotiate the best IaaS solution that is available in the cloud market and to build and deploy his/her cloud infrastructure.