IaaS Cloud Research Articles

Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT

More and more enterprises and communities choose cloud computing platforms to deploy their commercial or scientific workflow applications along with the increasing popularity of pay-as-you-go cloud services. A major task of cloud service providers is to minimize the monetary cost and makespan of executing workflows in the Infrastructure as a Service (IaaS) cloud. Most of the existing techniques for cost and makespan minimization are designed for traditional computing platforms which cannot be applied to the cloud computing platforms with unique service-based resource managing methods and pricing strategies. In this paper, we study the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and propose a novel workflow scheduling scheme. In this scheme, a fuzzy dominance sort based heterogeneous earliest-finish-time (FDHEFT) algorithm is developed which closely integrates the fuzzy dominance sort mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflows demonstrate the efficacy of our scheme. Our scheme can achieve significantly better cost-makespan tradeoff fronts with remarkably higher Hypervolume and can run up to hundreds of times faster than the state-of-the-art algorithms.

Multi-objective-Oriented Cuckoo Search Optimization-Based Resource Scheduling Algorithm for Clouds

Scheduling problems in cloud computing environment are mostly influenced by multi-objective optimization but frequently deal with using single-objective algorithms. The algorithms need to resolve multi-objective problems which are significantly different from the procedure or techniques used for single-objective optimizations. For this purpose, meta-heuristic algorithms always show their strength to deal with multi-objective optimization problems. In this research article, we present an innovative Multi-objective Cuckoo Search Optimization (MOCSO) algorithm for dealing with the resource scheduling problem in cloud computing. The main objective of resource scheduling problem is to reduce the cloud user cost and enhance the performance by minimizing makespan time, which helps to increase the revenue or profit for cloud providers with maximum resource utilization. Therefore, the proposed MOCSO algorithm is a new method for solving multi-objective resource scheduling problems in IaaS cloud computing environment. Moreover, the effects of the proposed algorithm are analyzed and evaluated by comparison with state-of-the-art multi-objective resource scheduling algorithms using simulation framework. Results obtained from simulation show that the proposed MOSCO algorithm performs better than MOACO, MOGA, MOMM and MOPSO, and balance multiple objectives in terms of expected time to completion and expected cost to completion matrices for resource scheduling in IaaS cloud computing environment.

Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment

Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard problem due to the fluctuating demand of cloud users and dynamic nature of resources. In this paper, we formulate a new hybrid gradient descent cuckoo search (HGDCS) algorithm based on gradient descent (GD) approach and cuckoo search (CS) algorithm for optimizing and resolving the problems related to resource scheduling in Infrastructure as a Service (IaaS) cloud computing. This work compares the makespan, throughput, load balancing and performance improvement rate of existing meta-heuristic algorithms with proposed HGDCS algorithm applicable for cloud computing. In comparison with existing meta-heuristic algorithms, proposed HGDCS algorithm performs well for almost in both cases (Case-I and Case-II) with all selected datasets and workload archives. HGDCS algorithm is comparatively and statistically more effective than ACO, ABC, GA, LCA, PSO, SA and original CS algorithms in term of problem solving ability in accordance with results obtained from simulation and statistical analysis.

LogDrive: a proactive data collection and analysis framework for time-traveling forensic investigation in IaaS cloud environments

This paper presents the LogDrive framework for mitigating the following problems of storage forensics in Infrastructure-as-a-Service (IaaS) cloud environments: volatility, increasing volume of forensic data, and anti-forensic attacks that hide traces of incidents in virtual machines. The proposed proactive data collection function of virtual block devices mitigates the problem of volatility within the cloud environments and enables a time-traveling investigation to reveal overwritten or deleted evidence files. We employ a sector-hash-based file detection method with random sampling to search for an evidence file in the record of the write logs of the virtual storage. The problem formulation, the investigation context, and the design with five algorithms are presented. We explore the performance of LogDrive through a detailed evaluation. Finally, security analysis of LogDrive is presented based on the STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privilege) threats model and related work. We posted the source code of LogDrive on GitHub.

Effective Modeling Approach for IaaS Data Center Performance Analysis under Heterogeneous Workload

Heterogeneity prevails not only among physical machines but also among workloads in real IaaS Cloud data centers (CDCs). The heterogeneity makes performance modeling of large and complex IaaS CDCs even more challenging. This paper considers the scenario where the number of virtual CPUs requested by each customer job may be different. We propose a hierarchical stochastic modeling approach applicable to IaaS CDC performance analysis under such a heterogeneous workload. Numerical results obtained from the proposed analytic model are verified through discrete-event simulations under various system parameter settings.

A prediction-Based VM consolidation approach in IaaS Cloud Data Centers

Recent years have witnessed a rapid growth in exploiting Cloud environments to host and deliver various types of virtualized resources as on-demand services. In order to optimally use Cloud resources, the arrangement of virtual machines (VMs) in physical machines (PMs) must be performed strategically, because the placement of VMs in accordance with the available resources can reduce energy consumption, improve resource utilization and, consequently, can increase companies benefits. However, VMs could have time varying workloads, which leads to degradation of performance and power consumption. Thus, re-configuring the VMs placement is essential. Virtual machine consolidation aims to optimally use the available resources by allocating several virtual machines on a set of physical ones (PMs). To determine the PMs capacities to reallocate VMs, it is important to predict their states based on resource utilization history within each VM, and the past VMs migration traffic. However, a common limitation between existing VM consolidation approaches is the lack of information about the history of (and the future) VM migration traffic. Through this paper, we aim to propose a virtual machine consolidation approach based on the estimation of requested resources and the future VM migration traffic. We exploit the strength of Kernel Density Estimation technique (KDE) as a powerful mean to forecast the future resource usage of each VM, and AKKA toolkit as an actor-based model that allows exchanging useful information about the host’s states. We adopt a weighted-graph representation to model the history of migration traffic between PMs and to design the actor-based topology of the data center. The obtained results show the effectiveness of our approach in terms of total number of migrations and energy consumption.

Multi-Objective Optimization for scientific workflow task scheduling in IaaS Cloud

The use of scientific applications on cloud networks increases day by day generating volumes of data and consuming large computational power. These scientific applications find its importance in the field of astronomy, geology, genetics and bio-technology etc. Complex and mission critical scientific applications can be modeled as scientific workflows and can be executed in cloud. The tasks of the scientific applications are generally data intensive and compute intensive. Traditional computer networks are not suitable for handling scientific applications and hence ubiquitous distributed networks like cloud are prominent in hosting scientific applications. The cloud hosted scientific applications and the cloud network need to satisfy many objectives to the interest of its users. This paper explores the multi-objective optimization applications in scientific workflow task scheduling in IaaS cloud and the related algorithms employed.

Implementation of a real-time network traffic monitoring service with network functions virtualization

The Network Functions Virtualization (NFV) extends the functionality provided by Software-Defined Networking (SDN). It is a virtualization technology that aims to replace the functionality provided by traditional networking hardware using software solutions. Thereby, enabling cheaper and efficient network deployment and management. The use of NFV and SDN is anticipated to enhance the performance of Infrastructure-as-a-Service (IaaS) clouds. However, due to the presence of a large number of network devices in IaaS clouds offering a plethora of networked services, there is need to develop a traffic monitoring system for the efficient network. This paper proposes and validates an extensible SDN and NFV-enabled network traffic monitoring system. Using extensive experiments, we show that the proposed system can closely match the performance of traditional networks at cheaper costs and by adding more flexibility to network management tasks.

Online Auction for IaaS Clouds: Towards Elastic User Demands and Weighted Heterogeneous VMs

Auctions have been adopted by many major cloud providers, such as Amazon EC2. Unfortunately, only simple auctions have been implemented. Such simple auction has serious limitations, such as being unable to accept elastic user demands and having to allocate different types of VMs independently. These limitations create a big gap between the real needs of cloud users and the available services of cloud providers. In response to the limitations of the existing auction mechanisms, this paper proposes a novel online auction mechanism for IaaS clouds, with the unique features of an elastic model for inputting time-varying user demands and a unified model for requesting heterogeneous VMs together. However, several major challenges should be addressed, such as NP hardness of optimal VM allocation, time-varying user demands and potential misreports of private information of cloud users. We propose a truthful online auction mechanism for maximizing the profit of the cloud provider in IaaS clouds, which is composed of a price-based allocation rule and a payment rule. In the allocation rule, the online auction mechanism determines the number of VMs of each type to each user. In the payment rule, by introducing a marginal price function for each type of VMs, the mechanism determines how much the cloud provider should charge each cloud user. With solid theoretical analysis and trace-driven simulations, we demonstrate that our mechanism is truthful, fair and individually rational, and has a polynomial-time complexity. In addition, our auction achieves a competitive ratio for the profit of the cloud provider, compared against the offline optimal one.

Data-Driven Auction Mechanism Design in IaaS Cloud Computing

With the emergence of big data computing and analysis, cloud computing services become more and more popular, which has recently drawn researchers’ great attentions to develop various new applications and mechanisms. In this paper, we consider the on-demand mechanism design in the infrastructure as a service (IaaS), including resource allocation and pricing issues under dynamic scenarios. Most of existing works on mechanism design assumed static and independent individual utility, while the cloud computing services are provided in a dynamic environment. To solve such problems, we start with analyzing the Google cluster-usage dataset to draw the statistical and stochastic characteristics of the IaaS consumers and providers. Based on the characteristics mined from real data, we propose a stochastic matching algorithm with Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency, with its online version using Q-learning method to address the imperfect model estimation problem. We further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism, which is an extension of traditional Vickrey-Clarke-Groves (VCG) mechanism. The proposed mechanism is studied under two application scenario: quality sensitive services, where unilateral MDP-VCG auction is implemented; and quality insensitive services, where MDP-VCG double auction is implemented. To verify the performance of our proposed mechanism, we conduct experiment using the Google dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.

Managing Risk in a Derivative IaaS Cloud

Infrastructure-as-a-Service (IaaS) cloud platforms rent computing resources with different cost and availability tradeoffs. For example, users may acquire virtual machines (VMs) in the spot market that are cheap, but can be unilaterally terminated by the cloud operator. Because of this revocation risk, spot servers have been conventionally used for delay and risk tolerant batch jobs. In this paper, we develop risk mitigation policies which allow even interactive applications to run on spot servers. Our System, SpotCheck is a derivative cloud platform, and provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports unmodified applications. SpotCheck’s design combines virtualization-based mechanisms for fault-tolerance, and bidding and server selection policies for managing the risk and cost. We implement SpotCheck on EC2 and show that it i) provides nested VMs with 99.9989 percent availability, ii) achieves upto 2-5 $\times$ cost savings compared to using on-demand VMs, and iii) eliminates any risk of losing VM state.

Power and resource-aware virtual machine placement for IaaS cloud

In the virtualization technology, cloud computing is a pool of abundant computing resources and delivers on-demand Internet-based computing services. One of the challenging issues in the virtualization is the placement of virtual machines (VMs) on the physical machines (PMs) such that the computing resources can be utilized efficiently. Furthermore, imbalanced usage of multi-dimensional resources may lead to overall resource wastage and SLA violations of a cloud data center. In this paper, we propose a new VM placement algorithm called multi-objective virtual machine placement (MOVMP) for IaaS cloud. In the algorithm, we devise a resource usage factor (RUF) to maximize the resource usage of the PMs during placement of the VMs. Further, we also present a multi-dimensional resource usage model, which direct to minimize the number of under-loaded PMs in IaaS cloud. This model also helps to improve resource utilization in a balanced manner and migrate a less number of VMs, which results in minimizing the resource wastage, power consumption, and the service level agreement (SLA) violations of cloud data center. The algorithm is tested using Amazon EC2 Instances. Through comparison results, we show that the proposed algorithm performs better than the existing ones in terms of various performance metrics.

A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.

Time-Cost Efficient Scheduling Algorithms for Executing Workflow in Infrastructure as a Service Clouds

Cloud Computing enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. The cloud infrastructures can be used as an appropriate environment for executing of workflow applications. To execute workflow applications in this environment, it is necessary to develop the workflow scheduling algorithms that consider different QoS parameters such as execution time and cost. Therefore, in this paper we focus on two criteria: total completion time (makespan) and execution cost of workflow, and propose two heuristic algorithms: MTDC (Minimum Time and Decreased Cost) which aims to create a schedule that minimizes the makespan and decreases execution cost, and CTDC (Constrained Time and Decreased Cost) which is based on the first algorithm (MTDC) and aims to create a schedule that decreases the execution cost while satisfying the deadline constraint of the workflow application. The proposed algorithms are evaluated by a simulation process using WorkflowSim. To evaluate the proposed algorithms, the results of MTDC are compared with the results of HEFT (Heterogeneous Earliest Finish Time), and the results of CTDC are compared with the results of heuristic based algorithms [such as IC-PCP (IaaS Cloud Partial Critical Paths), IC-PCPD2 (Deadline Distribution) and BDHEFT (Budget and Deadline HEFT)] and meta-heuristic based algorithms [such as PSO (Particle Swarm Optimization) and CGA2 (Coevolutionary Genetic Algorithm with Adaptive penalty function)]. The results show that the proposed algorithms perform better than the mentioned algorithms in most cases.

Business value-aware task scheduling for hybrid IaaS cloud

Since an Infrastructure as a Service (IaaS) provider has limited resources, it faces the challenge of scheduling tasks to meet the peak demand. For this issue, the task scheduling problem for hybrid cloud from an IaaS provider's perspective should be properly addressed. However, existing research does not consider either process tasks with slack temporal constraints or the business value from an IaaS provider's perspective, or it has to specify the type of Virtual Machine (VM) for each task prior. In this paper, we first formalize the problem in which both process tasks with slack temporal constraints and VM type selection optimization are considered, and business value is taken as the objective to comprehensively measure the earned value of an IaaS provider. Second, a novel Evolutionary Algorithm for Task Scheduling in a Hybrid IaaS Cloud (EATSHC) is proposed to solve the problem. Finally, several experiments show our approach performs more effectively and efficiently than existing research.

A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption

Cloud Computing has revolutionized the software, platform and infrastructure provisioning. Infrastructure-as-a-Service (IaaS) providers offer on-demand and configurable Virtual Machine (VMs) to tenants of cloud computing services. A key consolidation force that widespread IaaS deployment is the use of pay-as-you-go and pay-as-you-use cost models. In these models, a service price can be composed of two dimensions: the individual consumption, and a proportional value charged for service maintenance. A common practice for public providers is to dilute both capital and operational costs on predefined pricing sheets. In this context, we propose PSVE (Proportional-Shared Virtual Energy), a cost model for IaaS providers based on CPU energy consumption. Aligned with traditional commodity prices, PSVE is composed of two key elements: an individualized cost accounted from CPU usage of VMs (e.g., processing and networking), and a shared cost from common hypervisor management operations, proportionally distributed among VMs.

Building an IaaS cloud with droplets: a collaborative experience with OpenStack

The goal of a collaborative cloud is to promote resource sharing among different institutions using existing computational infrastructure. To accomplish that, we propose the PID (Platform for IaaS Distribution) cloud, where users from research institutions can benefit from IaaS services with minimum management and maintenance effort and with no subscription fees. The only requirement is to share at least one server capable of running virtual machines. To build PID, we rely on the OpenStack orchestrator as the basis of our implementation and, consequently, modifications are needed to accommodate the geo-distributed architecture. We present such modifications, which are concentrated on a different virtual machine scheduling, new hierarchical roles for users, and adapted web interfaces for management and control. Based on these modifications, we deploy a prototype in three universities in Rio de Janeiro. We experimentally evaluate PID regarding the latency between sites and the scalability according to the number of servers and virtual machines. Based on these experiments, we can estimate that an OpenStack cloud with a few hundred servers with tens of virtual machines each would add, on average, a few Mb/s of control traffic toward the Controller. We then conclude that the amount of control traffic generated by OpenStack services is not enough to surpass network capacities of our academic sites. Our results also show that the additional latency expected in a geo-distributed cloud has little or no impact on the OpenStack network component, called Neutron.

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.

Architectural Design of Trusted Platform for IaaS Cloud Computing

This article describes how the enormous potential benefits provided by the cloud services, made enterprises to show huge interest in adopting cloud computing. As the service provider has control over the entire data of an organization stored onto the cloud, a malicious activity, whether internal or external can tamper with the data and computation. This causes enterprises to lack trust in adopting services due to privacy, security and trust issues. Despite of having such issues, the consumer has no root level access right to secure and check the integrity of procured resources. To establish a trust between the consumer and the provider, it is desirable to let the consumer to check the procured platform hosted at provider side for safety and security. This article proposes an architectural design of a trusted platform for the IaaS cloud computing by the means of which the consumer can check the integrity of a guest platform. TCG's TPM is deployed and used on the consumer side as the core component of the proposed architecture and it is distributed between the service provider and the consumer.

A Study on Security Issues and Challenges in Cloud IaaS

A Study on Security Issues and Challenges in Cloud IaaS