Geo-distributed Cloud Research Articles

Multi-queue scheduling of heterogeneous jobs in hybrid geo-distributed cloud environment

In hybrid geo-distributed clouds, there is a technique named cloud bursting in which applications are handled in the private cloud with less expenses and burst into public clouds when the resources of the private cloud run out. However, how to deploy heterogeneous jobs in heterogeneous hybrid cloud environment is still a challenge. In this paper, a multi-queue scheduling approach of heterogeneous jobs for cloud bursting is proposed. In the private cloud, jobs are classified into I/O-intensive and CPU-intensive jobs, and nodes are divided into main I/O and CPU resource pools. Jobs are dispatched to corresponding resource pools to reduce the job execution time in heterogeneous cloud environment. A genetic algorithm is applied to schedule jobs to optimal job queues, which can reduce the job waiting time. Then, the execution time of each task is predicted by BP neural network. Jobs with high priority will be allocated to resources with the earliest finish time in the private cloud according to the prediction results. If the private cloud cannot meet the demand of users, public clouds with minimal costs will be applied. Experiments show that our proposed algorithm can reduce the average job response time and improve the throughput of the private cloud. It also can reduce the average task waiting time, average task execution time and average task response time significantly. Moreover, the costs of the hybrid clouds are reduced.

Providing Geo-Elasticity in Geographically Distributed Clouds

Geographically distributed cloud platforms are well suited for serving a geographically diverse user base. However, traditional cloud provisioning mechanisms that make local scaling decisions are not adequate for delivering the best possible performance for modern web applications that observe both temporal and spatial workload fluctuations. We propose GeoScale, a system that provides geo-elasticity by combining model-driven proactive and agile reactive provisioning approaches. GeoScale can dynamically provision server capacity at any location based on workload dynamics. We conduct a detailed evaluation of GeoScale on Amazon’s geo-distributed cloud and show up to 40% improvement in the 95th percentile response time when compared to traditional elasticity techniques.

Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market

This paper studies two design tasks faced by a geo-distributed cloud data market: which data to purchase (data purchasing) and where to place/replicate the data for delivery (data placement). We show that the joint problem of data purchasing and data placement within a cloud data market can be viewed as a facility location problem and is thus NP-hard. However, we give a provably optimal algorithm for the case of a data market made up of a single data center and then generalize the structure from the single data center setting in order to develop a near-optimal, polynomial-time algorithm for a geo-distributed data market. The resulting design, $\mathsf {Datum}$ , decomposes the joint purchasing and placement problem into two subproblems, one for data purchasing and one for data placement, using a transformation of the underlying bandwidth costs. We show, via a case study, that $\mathsf {Datum}$ is near optimal (within 1.6%) in practical settings.

A New Multi-objective Evolutionary Algorithm for Inter-Cloud Service Composition

Service composition in the Inter-Cloud raises new challenges that are caused by the different Quality of Service (QoS) requirements of the users, which are served by different geo-distributed Cloud providers. This paper aims to explore how to select and compose such services while considering how to reach high efficiency on cost and response time, low network latency, and high reliability across multiple Cloud providers. A new hybrid multi-objective evolutionary algorithm to perform the above task called LS-NSGA-II-DE is proposed, in which the differential evolution (DE) algorithm uses the adaptive mutation operator and crossover operator to replace the those of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to get the better convergence and diversity. At the same time, a Local Search (LS) method is performed for the Non-dominated solution set F{1} in each generation to improve the distribution of the F{1}. The simulation results show that our proposed algorithm performs well in terms of the solution distribution and convergence, and in addition, the optimality ability and scalability are better compared with those of the other algorithms.

Revenue Maximization for Dynamic Expansion of Geo-distributed Cloud Data Centers

In the cloud environment, it brings better reliability and robustness with geographically distributed datacenters. As the growth of large-scale applications in geo-distributed cloud systems, the resource demand from different areas increases violently, and researchers pay more attention to meet as many cloud users’ VM demands as possible by using limited cloud resources. However, there exist many issues for cloud users in existing works, such as the VM demands being refused and high response latency. In this paper, we present a cloud system model for the cloud provider to dynamically expand the scale of geo-distributed date centers. In our model, the cloud provider rents hardware resources from other resource owners (ROs), who have redundant resources and are willing to lease them. Since the ROs possess vast resources and spread all over the global, our system model can deploy more cloud users’ VMs and effectively reduce the bandwidth cost. We propose an optimization problem for the cloud provider to maximize the profit, and carefully solve it in different conditions. Our simulation results show that our system model and algorithms can effectively improve the user satisfaction and the total revenue and reduce the average latency of users’ requests.

Usage-Aware Protection Plan for State Management Functions in Service-Based 5G Core Network

In service-based 5G core networks, the mobile core functions can be decoupled as “stateless” control functions and state management functions to support fast failure recovery and independent scalability. With the arrival of the network function virtualization framework, these 5G mobile core functions can be deployed as virtual network functions over geo-distributed cloud infrastructures without much effort. With this new service-based 5G architecture, the availability of the core network depends on the availability of these state management functions. Therefore, protection plans are required to protect these state management functions. In this paper, we present a model to optimally place the standby functions protecting the active state management functions over geo-distributed cloud infrastructures. This model considers the constraints related to state management functions and considers a new factor (i.e., availability zones) in the estimation of the availability level of each protection plan. We focus on the optimization of total resources, including network bandwidth and computing resources. To minimize complexity, we propose a heuristic algorithm that achieves the optimal total resources while satisfying the availability requirements.

A Novel algorithm for improving Security and Storage for Online Social Networks on Geo-Distributed Clouds

A Novel algorithm for improving Security and Storage for Online Social Networks on Geo-Distributed Clouds

Efficient Scheduling of Scientific Workflows Using Hot Metadata in a Multisite Cloud

Large-scale, data-intensive scientific applications are often expressed as scientific workflows (SWfs). In this paper, we consider the problem of efficient scheduling of a large SWf in a multisite cloud, i.e., a cloud with geo-distributed cloud data centers (sites). The reasons for using multiple cloud sites to run a SWf are that data is already distributed, the necessary resources exceed the limits at a single site, or the monetary cost is lower. In a multisite cloud, metadata management has a critical impact on the efficiency of SWf scheduling as it provides a global view of data location and enables task tracking during execution. Thus, it should be readily available to the system at any given time. While it has been shown that efficient metadata handling plays a key role in performance, little research has targeted this issue in multisite cloud. In this paper, we propose to identify and exploit hot metadata (frequently accessed metadata) for efficient SWf scheduling in a multisite cloud, using a distributed approach. We implemented our approach within a scientific workflow management system, which shows that our approach reduces the execution time of highly parallel jobs up to 64 percent and that of the whole SWfs up to 55 percent.

A cost-effective cloud leasing policy for live streaming applications

The dynamics of the globalized multimedia sources and request demands, which requires high computations and bandwidths, makes the IT infrastructure a challenge for live streaming applications. Migrating the system to a geo-distributed cloud and leasing servers is an ideal alternative for supporting large-scale live streaming applications with dynamic contents and demands. The new challenge of multimedia live streaming applications in a geo-distributed cloud is how to efficiently arrange and migrate services among different cloud sites to guarantee the distribute users’ experience at modest costs. This paper first investigates cloud leasing policies for live streaming applications and finds that there is no detailed algorithm to help live streaming applications arrange and migrate services among different cloud sites. Then, we present a quality of service (QoS) guarantee cost-effective cloud leasing policy for live streaming applications. Meanwhile, we design a genetic algorithm (GA) to deal with the leasing policy among cloud sites of diverse lease prices. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA.

Dynamic Scaling of Virtualized, Distributed Service Chains: A Case Study of IMS

The emerging paradigm of network function virtualization advocates deploying virtualized network functions (VNF) on standard virtualization platforms for significant cost reduction and management flexibility. There have been system designs for managing dynamic deployment and scaling of VNF service chains within one cloud data center. Many real-world network services involve geo-distributed service chains, with prominent examples of mobile core networks and IMSs (IP Multimedia Subsystems). Virtualizing these service chains requires efficient coordination of VNF deployment across different geo-distributed data centers over time, calling for new management system design. This paper designs a dynamic scaling system for geo-distributed VNF service chains, using the case of an IMS. IMSs are widely used subsystems for delivering multimedia services among mobile users in a 3G/4G network, whose virtualization has been broadly advocated in the industry for reducing cost, improving network usage efficiency and enabling dynamic network topology reconfiguration for performance optimization. Our scaling system design caters to key control-plane and data-plane service chains in an IMS, combining proactive and reactive approaches for timely, cost-effective scaling of the service chains. We evaluate our system design using real-world experiments on both emulated platforms and geo-distributed clouds.

Optimal Dynamic Placement of Virtual Machines in Geographically Distributed Cloud Data Centers

In geo-distributed cloud systems, a key challenge faced by cloud providers is to optimally tune and configure the underlying cloud infrastructure. An important problem in this context, deals with finding an optimal virtual machine (VM) placement, minimizing costs, while at the same time, ensuring good system performance. Moreover, due to the fluctuations of demand and traffic patterns, it is crucial to dynamically adjust the VM placement scheme over time. It should be noted that most of the existing studies, however, dealt with this problem either by ignoring its dynamic aspect or by proposing solutions that are not suitable for a geographically distributed cloud infrastructure. In this paper, exact as well as heuristic solutions based on Integer Linear programming (ILP) formulations are proposed. Our work focuses also on the problem of scheduling the VM migration by finding the best migration sequence of intercommunicating VMs that minimizes the resulting traffic on the backbone network. The proposed algorithms execute within a reasonable time frame to readjust VM placement scheme according to the perceived demand. Our aim is to use VM migration as a tool for dynamically adjusting the VM placement scheme while minimizing the network traffic generated by VM communication and migration. Finally, we demonstrate the effectiveness of our proposed algorithms by performing extensive experiments and simulation.

Optimized provisioning of SDN-enabled virtual networks in geo-distributed cloud computing datacenters

Cloud computing provides on-demand IT services via large distributed datacenters over high-speed networks. Virtualization, a key cloud computing technology, allows service providers to offer computing services in cloud environments without platform compatibility discrepancies. The recent proliferation of cloud computing has rekindled interest in network virtualization. Thus, network virtualization is emerging as a polymorphic approach for the future Internet that will facilitate the use of shared resources. Virtual network provisioning is considered to be a main resource allocation challenge in any virtualized network environment. Software-defined networking (SDN) imparts flexibility to a network by removing the control layer from the data transfer layer of the network and moving it to the control plane. Network virtualization is further employed to share physical infrastructure to enable multiple service providers to access the network. Flexible access requires efficient management of network resources; the SDN control plane can be used for efficient management of virtual networks. In this study, we formulate virtual network provisioning in SDN-enabled, geographically distributed cloud computing datacenters as a mixed integer linear programming (MILP) problem. The formulation of the proposed optimized virtual network provisioning (OVNP) model is studied by means of simulations. The performance of the proposed approach is measured against enhanced network cloud provisioning (ENCP), our previous research, and other recognized research focused on the ratio of successfully provisioned requests and the efficiency of resource utilization The results verify the effectiveness of the proposed approach.

SDN-based energy management scheme for sustainability of data centers: An analysis on renewable energy sources and electric vehicles participation

Energy management is becoming one of the major issues from last many years due to an exponential increase in the smart device users for accessing various services from the geo-distributed cloud data centers (DCs) using Internet. During this time, there is an emergence of new technology called as cloud computing which provides on-demand pay-per-use services such as storage, computation, and network to the end users. These services are provided to the end users by various geographically located DCs which are hosted by different service providers such as—Microsoft, Amazon, IBM etc. With an increase in dependence of end users, DCs have expanded both in size and number. With such an expansion, these DCs consume huge amount of energy to accomplish their routine tasks. Such an increase in energy consumption generates a lot of load on the power grid. Moreover, the carbon emissions from these DCs has a global impact on the environment. To mitigate these issues, the integration of DCs with renewable energy sources (RES) can be helpful to reduce the carbon emissions and to ease the load on the power grid. For this purpose, a SDN-based energy management scheme for sustainability of DCs using RES is proposed in this paper.1 To achieve the aforementioned objectives, an energy-efficient flow scheduling algorithm is proposed using SDN. Moreover, a charging-discharging scheme for penetration of electric vehicles (EVs) is also presented to manage the intermittency of renewable energy. An energy trading and reward point scheme is designed to attract the EVs to participate in the proposed energy management scheme. The efficacy of the proposed scheme is proved using realistic weather traces. The results obtained clearly show the effectiveness of the proposed scheme for sustainability of DCs.

A Declarative Optimization Engine for Resource Provisioning of Scientific Workflows in Geo-Distributed Clouds

Geo-distributed clouds are becoming increasingly popular for cloud providers, and data centers with different regions often offer different prices, even for the same type of virtual machines. Resource provisioning in geo-distributed clouds is an important and complicated problem for budget and performance optimizations of scientific workflows. Scientists are facing the complexities resulted from various cloud offerings in the geo-distributed settings, severe cloud performance dynamics and evolving user requirements on performance and cost. To address those complexities, we propose a declarative optimization engine named Geco for resource provisioning of scientific workflows in geo-distributed clouds. Geco allows users to specify their workflow optimization goals and constraints of specific problems with an extended declarative language. We propose a novel probabilistic optimization approach for evaluating the declarative optimization goals and constraints to address the cloud dynamics. Additionally, we develop runtime optimizations to more effectively utilize the cloud resources at runtime. To accelerate the solution finding, Geco leverages the power of GPUs to find the solution in a fast and timely manner. Our evaluations with four common workflow provisioning problems demonstrate that, Geco is able to achieve more effective performance/cost optimizations in geo-distributed cloud environments than the state-of-the-art approaches.

Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing

New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinations of different performance levels and prices which can be configured at runtime. With such new pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem for cloud providers. CPU frequency scaling can be used to reduce power dissipation, but also impacts VM performance and therefore revenue. In this paper, we firstly propose a non-linear power model that estimates power dissipation of a multi-core PM and secondly a pricing model that adjusts the pricing based on the VM's CPU-boundedness characteristics. Finally, we present a cloud controller that uses these models to allocate VMs and scale CPU frequencies of the PMs to achieve energy cost savings that exceed service revenue losses. We evaluate the proposed approach using simulations with realistic VM workloads, electricity price and temperature traces and estimate energy savings of up to 14.57%.

Resilient application placement for geo-distributed cloud networks

The strong uptake of cloud computing has led to an important increase of mission-critical applications being placed on cloud environments. Those applications often require high levels of availability coupled with guarantees on a minimum level of throughput and a maximum level of response time. To achieve the lowest response time possible, clouds are more and more decentralized, leading to a heterogeneous network of micro clouds positioned on the edge of the network and possibly interconnected by best-effort links. This heterogeneous environment introduces important challenges for the management of these clouds as the heterogeneity results in an increased failure probability. In this paper, we address these challenges by providing a resilient placement of mission-critical applications on geo-distributed clouds. We present an exact solution to the problem, which is complemented by two heuristics: a near-optimal distributed genetic meta-heuristic and a scalable centralized heuristic based on subgraph isomorphism detection. A detailed performance evaluation shows that, with the newly proposed heuristic based on subgraph isomorphism detection, we can double the amount of applications satisfying availability requirements, in cloud environments comprising over 100 nodes, while keeping the time required to calculate the solution under 20s.

Carbon-Aware Online Control of Geo-Distributed Cloud Services

Recently, datacenter carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of datacenters to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed datacenters. Specifically, we first verify that electricity cost minimization conflicts with carbon emission minimization, based on an empirical study of several representative geo-distributed cloud services. We then jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analysis and real-world trace-driven evaluation demonstrate the effectiveness of our framework in reducing both electricity cost and carbon emission.

Joint Data Purchasing and Data Placement in a Geo-Distributed Data Market

This paper studies design challenges faced by a geo-distributed cloud data market: which data to purchase (data purchasing) and where to place/replicate the data (data placement). We show that the joint problem of data purchasing and data placement within a cloud data market is NP-hard in general. However, we give a provably optimal algorithm for the case of a data market made up of a single data center, and then generalize the structure from the single data center setting and propose Datum, a near-optimal, polynomial-time algorithm for a geo-distributed data market.

Coping With Heterogeneous Video Contributors and Viewers in Crowdsourced Live Streaming: A Cloud-Based Approach

With the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, end-users are no longer pure content consumers, but contributors, too. In today's crowdsourced streaming systems, numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are geo-distributed globally and highly heterogeneous in terms of the generated video quality and the network/system configurations. The scalability and heterogeneity challenges therefore lie on both broadcasters and the viewers, which call for massive transcoding, and two critical issues: 1) choosing video representation set that maximizes viewer satisfaction and 2) allocating computational resources that minimize operational costs, must be systematically optimized in the global scale. In this paper, we present a generic framework utilizing the powerful and elastic cloud computing services for crowdsourced live streaming with heterogeneous broadcasters and viewers. We jointly consider the viewer satisfaction and the service availability/pricing of geo-distributed cloud resources for transcoding. We develop an optimal scheduler for allocating cloud instances with no regional constraints. We then extend the solution to accommodate regional constraints, and discuss a series of practical enhancements, including popularity forecasting, initialization latency, and viewer feedbacks. Our solutions have been evaluated under diverse networks and cloud system configurations as well as parameter settings. The trace-driven simulation confirms the superiority of our design, while our Planetlab-based experiment offers further practical hints toward real-world migration.

Optimizing Cost for Online Social Networks on Geo-Distributed Clouds

Geo-distributed clouds provide an intriguing platform to deploy online social network (OSN) services. To leverage the potential of clouds, a major concern of OSN providers is optimizing the monetary cost spent in using cloud resources while considering other important requirements, including providing satisfactory quality of service (QoS) and data availability to OSN users. In this paper, we study the problem of cost optimization for the dynamic OSN on multiple geo-distributed clouds over consecutive time periods while meeting predefined QoS and data availability requirements. We model the cost, the QoS, as well as the data availability of the OSN, formulate the problem, and design an algorithm named cosplay. We carry out extensive experiments with a large-scale real-world Twitter trace over 10 geo-distributed clouds all across the US. Our results show that, while always ensuring the QoS and the data availability as required, cosplay can reduce much more one-time cost than the state-of-the-art methods, and it can also significantly reduce the accumulative cost when continuously evaluated over 48 months, with OSN dynamics comparable to real-world cases.