Heterogeneous Cloud Platforms Research Articles

Cost and performance aware scheduling technique for cloud computing environment

Recently, lot of interest have been put forth by researchers to improve workload scheduling in cloud platform. However, execution of scientific workflow on cloud platform is time consuming and expensive. As users are charged based on hour of usage, lot of research work have been emphasized in minimizing processing time for reduction of cost. However, the processing cost can be reduced by minimizing energy consumption especially when resources are heterogeneous in nature; very limited work have been done considering optimizing cost with energy and processing time parameters together in meeting task quality of service (QoS) requirement. This paper presents cost and performance aware workload scheduling (CPA-WS) technique under heterogeneous cloud platform. This paper presents a cost optimization model through minimization of processing time and energy dissipation for execution of task. Experiments are conducted using two widely used workflow such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost in comparison with standard workload scheduling model.

An efficient resource utilization technique for scheduling scientific workload in cloud computing environment

<span lang="EN-US">Recently, number of data intensive workflow have been generated with growth of internet of things (IoT’s) technologies. Heterogeneous cloud framework has been emphasized by existing methodologies for executing these data-intensive workflows. Efficient resource scheduling plays a very important role provisioning workload execution on Heterogeneous cloud framework. Building tradeoff model in meeting energy constraint and workload task deadline requirement is challenging. Recently, number of multi-objective-based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these models induce significant overhead when demand and number of processing core increases. For addressing research problem here, the workload is modelled by considering each sub-task require dynamic memory, cache, accessible slots, execution time, and I/O access requirement. Thus, for utilizing resource more efficiently better cache resource management is needed. Here efficient resource utilization (ERU) model is presented. The ERU model is designed to utilize cache resource more efficiently and reduce last level cache failure and meeting workload task deadline prerequisite. The ERU model is very efficient when compared with standard resource management methodology in terms of reducing execution time, power consumption, and energy consumption for execution scientific workloads on heterogeneous cloud platform.</span>

A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

Load balancing techniques in cloud computing can be applied at three different levels: Virtual machine load balancing, task load balancing, and resource load balancing. At all levels, load balancing should also be implemented in an efficient manner, to increase system performance. In this paper, we propose a fair, in terms of added workload per VM, task load balancing strategy, that aims to improve the average response time and the makespan of the system in the cloud environment. The problem is formulated as an irreducible finite state Markov process, which is known to have a balance equation for each state. From the balance state probabilities we derive the expected utilizations for the virtual machines (VM), which play a vital role in our task allocation approach. In our model, the Load Balancer (LBer) acts as a central server, which uses our proposed fair task allocation scheme to distribute the incoming tasks in a fair, balanced manner among the virtual machines, taking into account their current state as well as their processing capabilities. Our scheme has been compared to recent algorithms that use the particle swarm optimization and the Honey bee foraging scheme to achieve load balancing. Our experimental results show that our proposed scheme outperforms other state of the art schemes in terms of makespan, average response time, and resource utilization and provides lower degree of imbalance.

An archetype for mitigating the security threats in multi-cloud environment by implementing tree-based next-generation firewalls

Cloud computing on-demand dynamicity in nature of end-user that leads towards a hybrid cloud model deployment is called a multi-cloud. Multi-cloud is a multi-tenant and multi-vendor heterogeneous cloud platform in terms of services and security under a defined SLA (service level agreement). The diverse deployment of the multi-cloud model leads to rise in security risks. In this paper, we define a multi-cloud model with hybridization of vendor and security to increase the end-user experience. The proposed model has a heterogeneous cloud paradigm with a combination of firewall tracts to overcome rising security issues. The proposed work consists of three steps, firstly, all incoming traffic from the consumer end into five major groups called ambient. Secondly, design a next-generation firewall (NGFW) topology with a mixture of tree-based and demilitarized zone (DMZ) implications. Test implementation of designed topology performed by using a simple DMZ technique in case of vendor-specific model and NGFW on hybrid vendor based multi-cloud model. Furthermore, it also defines some advantages of NGFW to overcome these concerns. The proposed work is helpful for the new consumer to define their dynamic secure cloud services under a single SLA before adopting a multi-cloud platform. Finally, results are compared in terms of throughput and CPU utilization in both cases.

SDN-NFV-Aided Edge-Cloud Interplay for 5G-Envisioned Energy Internet Ecosystem

Energy Internet (also referred to as Smart Grid 2.0) is another promising application of the Industrial Internet of Things (IIoT), for example, in the way energy is being produced, traded, distributed, and consumed. This is partly due to the lowering of barriers (e.g., costs and Internet connectivity) and advances in the underlying technologies, such as smart meters, electric vehicles, and actuators. This has also resulted in significant growth in the volume, velocity, variety, veracity, and value of data (i.e., the 5 Vs of big data). However, efficiently and effectively handling such big data remains challenging. One solution currently being explored in the literature (including industry) to cope with the increasing network traffic is to use conventional cloud infrastructure, but the key limitations of such an approach include long response time and high bandwidth consumption. Therefore, there have been attempts to introduce next-generation Internet of Things networks to satisfy (real-time) network service demands and guarantee quality of service, for example, by pushing computing capabilities closer to the users (e.g., edge of the network). However, computation-intensive energy analytics can be challenging to perform at the network edge by edge or fog computing devices. Hence, there have also been attempts to utilize software defined networking (SDN) and network function virtualization (NFV) in order to improve network functionality while adding programmability and flexibility features to the network infrastructure. Specifically, NFV facilitates virtual network function (VNF) deployment and orchestration, while SDN controls them for specific use cases. However, with the rise of next-generation mobile networks (i.e., 5G), applications and services require fast and smooth operations with greater flexibility, efficiency, and scalability. In order to align with 5G and leverage potential benefits of edge computing, VNFs should possess critical processing requirements (e.g., high throughput, low latency, and minimal computation overheads). In other words, virtualization plays an important role. To date, several techniques have been introduced in order to achieve the desired objective using virtual machines (VMs) and containers in isolation. However, a hybrid approach using VMs and containers is likely to bring potential benefits for the large-scale deployment of VNFs across the heterogeneous edge and cloud platform. Thus, in this article, a novel architecture for SDN integrated with NFV, specifically for the Energy Internet ecosystem, is presented by leveraging the advantagesof edge computing. In the considered setup, the deployment of VNFs is achieved using a mix of both virtualization and containerization. Findings from our evaluation demonstrate the potential for VNF placement across a hybrid execution setup powered by VMs, as well as the benefits of using containers.

Component‐aware orchestration of cloud‐based enterprise applications, from TOSCA to Docker and Kubernetes

SummaryEnterprise IT is currently facing the challenge of coordinating the management of complex, multicomponent applications across heterogeneous cloud platforms. Containers and container orchestrators provide a valuable solution to deploy multicomponent applications over cloud platforms, by coupling the lifecycle of each application component to that of its hosting container. We hereby propose a solution for going beyond such a coupling, based on the OASIS standard TOSCA and on Docker. We indeed propose a novel approach for deploying multicomponent applications on top of existing container orchestrators, which enables managing each component independently from the container used to run it. We also present prototype tools that implement our approach, and we show how we effectively exploited them to carry out a concrete case study.

Cost-aware orchestration of applications over heterogeneous clouds.

The orchestration of applications and their components over heterogeneous clouds is recognized as being critical in solving the problem of vendor lock-in with regards to distributed and cloud computing. There have been recent strides made in the area of cloud application orchestration with emergence of the TOSCA standard being a definitive one. Although orchestration by itself provides a considerable amount of benefit to consumers of cloud computing services, it remains impractical without a compelling reason to ensure its utilization by cloud computing consumers. If there is no measurable benefit in using orchestration, then it is likely that clients may opt out of using it altogether. In this paper, we present an approach to cloud orchestration that aims to combine an orchestration model with a cost and policy model in order to allow for cost-aware application orchestration across heterogeneous clouds. Our approach takes into consideration the operating cost of the application on each provider, while performing a forward projection of the operating cost over a period of time to ensure that cost constraints remain unviolated. This allows us to leverage the existing state of the art with regards to orchestration and model-driven approaches as well as tie it to the operations of cloud clients in order to improve utility. Through this study, we were able to show that our approach was capable of providing not only scaling features but also orchestration features of application components distributed across heterogeneous cloud platforms.

Adaptive management of applications across multiple clouds: The SeaClouds Approach


 
 
 How to deploy and manage, in an efficient and adaptive way, complex applications across multiple heterogeneous cloud platforms is one of the problems that have emerged with the cloud revolution. In this paper we present context, motivations and objectives of the EU research project SeaClouds, which aims at enabling a seamless adaptive multi-cloud management of complex applications by supporting the distribution, monitoring and migration of application modules over multiple heterogeneous cloud platforms. After positioning SeaClouds with respect to related cloud initiatives, we present the SeaClouds architecture and discuss some of its aspect, such as the use of the OASIS standard TOSCA and the compatibility with the OASIS CAMP initiative.
 
 

A loosely coupled scalable cloud infrastructure

Large-scale virtual computing system requires a loosely coupled virtual resource management solution. The solution should be able to flexibly increase or decrease virtual resources and to smooth upgrade the cloud platform. OpenStack provides large-scale virtualization solution such as Cells v2.0 and Tricircle/Trio2o. But it’s so complex and difficult to be deployed and maintain for small cloud computing teams. We discuss a loosely coupled cloud infrastructure. It is based on a central database and plugin to achieve information collection, virtual machine scheduling and controlling and uses a simplified method to achieve unified management of network and images. It can flexibly expand or reduce cluster members, deploy different versions of OpenStack and even a heterogeneous cloud platform. The test bed works well at IHEP. We will show and analyse the infrastructure.

TosKer: A synergy between TOSCA and Docker for orchestrating multicomponent applications

SummaryHow to flexibly manage complex applications across heterogeneous cloud platforms is one of the main concerns in today's IT enterprise. The OASIS standard TOSCA (Topology and Orchestration Specification for Cloud Applications) and the Docker ecosystem are two emerging solutions trying to address this problem from different perspectives. In this paper, we propose a solution that tries to synergically combine the pros of both TOSCA and of Docker. More precisely, we propose a TOSCA‐based representation for specifying the software components and the Docker containers forming an application. We also present TosKer, an engine for orchestrating the management of multicomponent applications based on the proposed TOSCA representation and on Docker. Finally, we illustrate how TosKer was fruitfully exploited in a concrete case study based on a third‐party application.

When Clones Flock Near the Fog

FogMQ is a message brokering and device cloning service in fog and edge computing. Excessive tail end-to-end latency occurs with conventional message brokers when a massive number of geographically distributed devices communicate through a message broker. Latency of broker-less messaging is highly dependent on computational resources of devices. Device-to-device messaging does not necessarily ensure low messaging latency and cannot scale well for a large number of resource-limited and geographically distributed devices. For each device, FogMQ provides a high capacity device cloning service that subscribes to device messages. The clones facilitate near-the-edge data analytics in resourceful cloud compute nodes. Clones in FogMQ apply Flock; an algorithm mimicking flocking-like behavior and allows the clones to autonomously migrate between heterogeneous cloud platforms. Flock controls and minimizes the weighted tail end-to-end latency. We have implemented FogMQ and evaluated it in a geographically distributed testbed. In our functional evaluation, we show that FogMQ is stable and achieves a bounded tail end-to-end latency that is up to 34% less than existing brokering methods.

Dynamic cloud resource management for efficient media applications in mobile computing environments

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.

Budget‐constraint stochastic task scheduling on heterogeneous cloud systems

SummaryIn the past few years, more and more business‐to‐consumer and enterprise applications run in the heterogeneous clouds. Such cloud bag‐of‐tasks applications are usually budget constrained, and their scheduling is an essential problem for cloud provider. The problem is even more complex and challenging when the accurate knowledge about task execution time is unknown in advance. Focusing on these challenges, we first build a cloud resource management architecture and stochastic task model, which divides cloud task into two execution parts. Then, we deduce bag‐of‐tasks applications' schedule length (Makespan) and total cost according to heterogeneous clouds' online feedback information of task first part execution. Thirdly, we formulate this stochastic scheduling problem as a linear programming problem. Lastly, we propose a time and cost multiobjective stochastic task scheduling genetic algorithm, in which can find Pareto optimal schedules for stochastic cloud task that meet its budget constraint. The extensive simulation experiments were carried out on a heterogeneous cloud platform with 400 virtual machines, and tasks were derived from Parallel Workloads Archive and the analysis data of real‐world cloud systems. The experimental results show that our proposed stochastic task scheduling genetic algorithm can get shorter schedule length and lower cost with task budget constraints.

Cross-Site Virtual Network in Cloud and Fog Computing

The interconnection of the different geographically dispersed cloud and fog infrastructures is a key issue for the development of the fog technology. Although most existing cloud providers and platforms offer some kind of connectivity services to allow the interconnection with external networks, these services exhibit many limitations and they are not suitable for fog computing environments. In this work we present a hybrid fog and cloud interconnection framework, which allows the automatic provision of cross-site virtual networks to interconnect geographically distributed cloud and fog infrastructures. This framework provides a scalable and multi-tenant solution, and a simple and generic interface for instantiating, configuring and deploying Layer 2 and Layer 3 overlay networks across heterogeneous fog and cloud platforms, with abstraction from the underlying cloud/fog technologies and network virtualization technologies.

Optimization of traffic distribution control in software-configurable infrastructure of virtual data center based on a simulation model

Currently, the proportion of use of cloud computing technology in today's business processes of companies is growing steadily. Despite the fact that it allows you to reduce the cost of ownership and operation of IT infrastructure, there are a number of problems related to the control of data centers. One such problem is the efficiency of the use of available companies compute and network resources. One of the directions of optimization is the process of traffic control of cloud applications and services in data centers. Given the multi-tier architecture of modern data center, this problem does not quite trivial. The advantage of modern virtual infrastructure is the ability to use software-configurable networks and software-configurable data storages. However, existing solutions with algorithmic optimization does not take into account a number of features forming network traffic with multiple classes of applications. Within the framework of the exploration solved the problem of optimizing the distribution of traffic cloud applications and services for the software-controlled virtual data center infrastructure. A simulation model describing the traffic in data center and software-configurable network segments involved in the processing of user requests for applications and services located network environment that includes a heterogeneous cloud platform and software-configurable data storages. The developed model has allowed to implement cloud applications traffic management algorithm and optimize access to the storage system through the effective use of the channel for data transmission. In experimental studies found that the application of the developed algorithm can reduce the response time of cloud applications and services, and as a result improve the performance of processing user requests and to reduce the number of failures.

Encryption-Based Solution for Data Sovereignty in Federated Clouds

The rapidly growing demand for cloud services in the current business practice has favored the success of the hybrid clouds and the advent of cloud federation. The available literature of this topic has focused on middleware abstraction to interoperate heterogeneous cloud platforms and orchestrate different management and business models. However, cloud federation implies serious security and privacy issues with respect to data sovereignty when data is outsourced across different judicial and legal systems. This column describes a solution that applies encryption to protect data sovereignty in federated clouds rather than restricting the elasticity and migration of data across federated clouds.

RETRACTED ARTICLE: A New Trust Evaluation Algorithm Between Cloud Entities Based on Fuzzy Mathematics

High security of cloud computing is one of the most challenges to be addressed before the novel pas-as-you-go business paradigm is widely applied over the internet. Trust brings a novel means to improve the security and enable interoperability of current heterogeneous independent cloud platforms. However, there is no special trust evaluation model for cloud computing environment. Hence, this paper presents a new trust model based on fuzzy mathematics in cloud computing environment according to success and failure interaction between cloud entities based on the properties and semantics of trust. To compute trust in cloud systems, an algorithm based on proposed model is given. Simulation results show that the proposed model has some identification and containment capability in synergies cheating, promotes interaction between entities, and improves the performance of the entire cloud environment.

Trust Model To Enhance Security Of Cloud Computing

Trust is one of the most important means to improve security and enable interoperability of current heterogeneous independent cloud platforms. Trust is a level of subjective probability between two entities, a trustor and a trustee, which is formed through the direct observation nature and/or recommendation from trusted entities. Today, there is no special trust evaluation model for cloud computing environment. Hence, in this paper, we present a trust model based on fuzzy mathematics in cloud computing environment according to success and failure interaction between cloud entities.

An inter-cloud bridge system for heterogeneous cloud platforms

Over the years, more cloud computing systems have been developed providing flexible interfaces for inter-cloud interaction. This work approaches the concept of inter-cloud by utilizing APIs, open source specifications and exposed interfaces from cloud platforms such as OpenStack, OpenNebula and others. Despite other works in the area of inter-cloud, that are mainly resource management-centric, we focus on designing and developing a service-centric architecture. We implement an inter-cloud bridge system that is elastic, easy to be upgraded and managed. We develop a prototype composed not only from heterogeneous cloud platforms but also from independent cloud services. These are developed by different cloud service providers and offered as open source Software as a Service (SaaS). The proposed Inter-Cloud Mediation Service uses Future Internet SaaS such as a Context Broker for registrations and subscriptions to services and a Complex Event Processing engine for event management. We present an experimental analysis to show interactions with various heterogeneous cloud platforms and we evaluate the performance of inter-cloud services separately and as a whole.

Energy Efficiency Oriented Scheduling for Heterogeneous Cloud Systems

Nowadays, how to improve energy efficiency has become a challenging problem in cloud computing. However, most existing efforts in improving the energy efficiency of a cloud system only focus on resource allocation at the system level like managing physical nodes or virtual machines. This paper tries to address the energy efficiency problem of a heterogeneous cloud system at the task scheduling level. A novel task scheduling algorithm is proposed to reduce the energy consumption of the system while maintaining its performance, without closing or consolidating any system resources such as virtual machines or storage systems. The algorithm dynamically monitors CPU and memory load information of participating nodes on a heterogeneous Hadoop platform with Ganglia, then selects and submits an appropriate task to the node with relatively low workload to avoid excessive energy consumption on some nodes. Experimental results show that this novel scheduling algorithm can effectively improve the energy-saving ratio of a heterogeneous cloud platform while maintaining a high system performance.