GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications

Executing bag-of-tasks applications in multiple Cloud environments while satisfying both consumers’ budgets and deadlines poses the following challenges: How many resources and how many hours should be allocated? What types of resources are required? How to coordinate the distributed execution of bag-of-tasks applications in resources composed from multiple Cloud providers?. This work proposes a genetic algorithm for estimating suboptimal sets of resources and an agent-based approach for executing bag-of-tasks applications simultaneously constrained by budgets and deadlines. Agents (endowed with distributed algorithms) compose resources and coordinate the execution of bag-of-tasks applications. Empirical results demonstrate that the genetic algorithm can autonomously estimate sets of resources to execute budget-constrained and deadline-constrained bag-of-tasks applications composed of more economical (but slower) resources in the presence of loose deadlines, and more powerful (but more expensive) resources in the presence of large budgets. Furthermore, agents can efficiently and successfully execute randomly generated bag-of-tasks applications in multi-Cloud environments.     

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end‐users under a usage‐based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter‐networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter‐networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy‐efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.     

Isolation im Cloud-Computing und Mechanismen zum Schutz der Privatsph?re

Cloud Computing lifts the borders between the access control domain of individuals?? and companies?? IT systems by processing their data within the application frameworks and virtualized runtime environments of Cloud service providers. A deployment of traditional security policies for enforcing confidentiality of Cloud users?? data would lead to a conflict with the availability of the Cloud??s software services: confidentiality of data would be assured but Cloud services would not be available for every user of a Cloud. This state-of-the-art contribution shows the analogy of the confidentiality of external data processing by Cloud services with mechanisms known and applied in privacy. Sustainability in Cloud is a matter of privacy, which in Cloud is called ??isolation??.     

Quality of Experience in Cloud services: Survey and measurements

Cloud-based systems are gaining enormous popularity due to a number of promised benefits, including ease of use in terms of deployment, administration and maintenance, high scalability as well as flexibility to create new services. However, as more personal and business applications migrate to the Cloud, the service quality becomes an important differentiator between providers, specially in the case of mobile operators. Quality of Experience (QoE) as perceived by the end-user has therefore the potential to become the guiding paradigm for managing quality provisioning and applications’ design in the Cloud. This paper presents the results of several Cloud QoE studies performed for different Cloud-based services, ranging from services with low requirements in terms of latency and interactivity (e.g., Cloud storage systems), multimedia On-Demand services (e.g., YouTube video streaming), communication and telepresence (e.g., Lync Online videoconferencing) to highly interactive services (e.g., Virtual Cloud Desktop). The results of these studies provide a ground truth basis for developing future Cloud services with QoE requirements, as well as for dimensioning the underlying network provisioning infrastructures, particularly with regard to mobile access technologies.     

Proactive virtualized resource management for service workflows in the cloud

A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.     

Agent-based optimisation of logistics and production planning

Manufacturing and logistics service provision enterprises are currently moving towards open virtual enterprise collaboration networks to meet the needs of the Global Economy. In such networks, manufacturing and logistics planning and scheduling is challenging due to the difficulties in integrating information from different partners and in exploring a large and dynamically changing number of planning and scheduling alternatives. Agent-based technology is considered suitable to support planning and scheduling in such enterprises because agents can dynamically adapt their behaviour to changing requirements and they can reduce the number of planning and scheduling alternatives via negotiation.This paper presents an agent-based approach for supporting logistics and production planning, taking into account not only production schedules but also availability and cost of logistic service providers. This is achieved through efficient negotiation mechanisms based on an extended contracting protocol. The agent infrastructure is being developed within the context of Agentcities, a successful EU-funded initiative to build a world-wide distributed and open platform which provides agent-based services.The proposed approach is illustrated in a case study concerning optimisation of production planning of a virtual manufacturing enterprise in relation to sub-contracted logistic services used to transport materials between the enterprise units.     

Dynamic,behavioral-based estimation of resource provisioning based on high-level application terms in Cloud platforms

Delivering Internet-scale services and IT-enabled capabilities as computing utilities has been made feasible through the emergence of Cloud environments. While current approaches address a number of challenges such as quality of service, live migration and fault tolerance, which is of increasing importance, refers to the embedding of users’ and applications’ behaviour in the management processes of Clouds. The latter will allow for accurate estimation of the resource provision (for certain levels of service quality) with respect to the anticipated users’ and applications’ requirements. In this paper we present a two-level generic black-box approach for behavioral-based management across the Cloud layers (i.e., Software, Platform, Infrastructure): it provides estimates for resource attributes at a low level by analyzing information at a high level related to application terms (Translation level) while it predicts the anticipated user behaviour (Behavioral level). Patterns in high-level information are identified through a time series analysis, and are afterwards translated to low-level resource attributes with the use of Artificial Neural Networks. We demonstrate the added value and effectiveness of the Translation level through different application scenarios: namely FFMPEG encoding, real-time interactive e-Learning and a Wikipedia-type server. For the latter, we also validate the combined level model through a trace-driven simulation for identifying the overall error of the two-level approach.     

COCOA: COnversation-based service COmposition in pervAsive computing environments with QoS support

Pervasive computing environments are populated with networked services, i.e., autonomous software entities, providing a number of functionalities. One of the most challenging objectives to be achieved within these environments is to assist users in realizing tasks that integrate on the fly functionalities of the networked services opportunely according to the current pervasive environment. Towards this purpose, we present COCOA, a solution for COnversation-based service COmposition in pervAsive computing environments with QoS support. COCOA provides COCOA-L, an OWL-S based language for the semantic, QoS-aware specification of services and tasks, which further allows the specification of services and tasks conversations. Moreover, COCOA provides two mechanisms: COCOA-SD for the QoS-aware semantic service discovery and COCOA-CI for the QoS-aware integration of service conversations towards the realization of the user task’s conversation. The distinctive feature of COCOA is the ability of integrating on the fly the conversations of networked services to realize the conversation of the user task, by further meeting the QoS requirements of user tasks. Thereby, COCOA allows the dynamic realization of user tasks according to the specifics of the pervasive computing environment in terms of available services and by enforcing valid service consumption.     

Context-aware handoff middleware for transparent service continuity in wireless networks

Advances in wireless networking and content delivery are enabling new challenging provisioning scenarios where a growing number of users access continuous services, e.g., audio/video streaming, while moving among different points of attachment to the Internet, possibly with different connectivity technologies, e.g., Wi-Fi, Bluetooth, and cellular 3G. That calls for novel middlewares capable of dynamically personalizing service provisioning to the characteristics of client environments, in particular to discontinuities in wireless resource availability due to handoffs. The paper proposes a novel middleware solution, called Mobile agent-based Ubiquitous multimedia Middleware (MUM), that performs effective and context-aware handoff management to transparently avoid service interruptions during both horizontal and vertical handoffs. MUM exploits the full visibility of wireless connections available and their handoff implementations (handoff awareness), of service quality requirements and handoff-related quality degradations (QoS awareness), and of network topology and local resource availability (location awareness); that visibility enables MUM to provide original solutions for handoff prediction, multimedia continuity via adaptive data buffering/pre-fetching, and proactive re-addressing/rebinding.     

Hybrid approach for context-aware service discovery in healthcare domain

Context-awareness computing is a research field which often refers to healthcare as an interesting and rich area of application. Context aware computing attains environments monitoring by means of sensors to provide relevant information or services according to the identified context. In particular, wireless ad hoc sensor networks for medical purposes are playing an increasing role within healthcare. Body Sensor Networks (BSN) are being designed for prophylactic and follow-up monitoring of patients in e.g. their homes, during hospitalization, and in emergencies. This work presents an integrated environment aimed at providing personalized healthcare services which appropriately meet the user?s context. Deploying the semantics embedded in web services and context models is a mandatory step in the automation of service discovery, invocation and composition. Nevertheless, in a context aware domain purely logic-based reasoning on respectively context and services may not be enough. The main idea of this work is related to enrich with qualitative representation of context underling data by means of Fuzzy Logic in order to automatically recognize the context and to consequently find the right set of healthcare services among the available ones. Semantic formalisms (e.g., OWL, OWL-S, etc.) enable the context and services modeling in terms of domain ontology concepts. On the other hand, soft computing techniques support activity of unsupervised context analysis and healthcare semantic service discovery. Goal is to define context-aware system whose quality of retrieved services relies on the acquisition of user context by means of a robust theoretical approach. Moreover, this work defines hybrid architecture which attains a synergy between the agent-based paradigm and the fuzzy modeling. Specifically, the system exploits some task oriented agents in order to achieve context recognition, services matchmaking and brokerage activities.     

Weighted principal component analysis-based service selection method for multimedia services in cloud

Cloud computing has rendered its ever-increasing advantages in flexible service provisions, which attracts the attentions from large-scale enterprise applications to small-scale smart uses. For example, more and more multimedia services are moving towards cloud to better accommodate people’s daily uses on various smart devices that support cloud, some of which are similar or equivalent in their functionality (e.g., more than 1,000 video services that share similar “video-play” functionality are present in APP Store). In this situation, it is necessary to discriminate these functional-equivalent multimedia services, based on their Quality of Service (QoS) information. However, due to the abundant information of multimedia content, dozens of QoS criteria are often needed to evaluate a multimedia service, which places a heavy burden on users’ multimedia service selection. Besides, the QoS criteria of multimedia services are usually not independent, but correlated, which cannot be accommodated very well by the traditional selection methods, e.g., traditional simple weighting methods. In view of these challenges, we put forward a multimedia service selection method based on weighted Principal Component Analysis (PCA), i.e., Weighted PCA-based Multimedia Service Selection Method (W_PCA_MSSM). The advantage of our proposal is two-fold. First, weighted PCA could reduce the number of QoS criteria for evaluation, by which the service selection process is simplified. Second, PCA could eliminate the correlations between different QoS criteria, which may bring a more accurate service selection result. Finally, the feasibility of W_PCA_MSSM is validated, by a set of experiments deployed on real-world service quality set QWS Dataset.     

Enhancing Federated Cloud Management with an Integrated Service Monitoring Approach

Cloud Computing enables the construction and the provisioning of virtualized service-based applications in a simple and cost effective outsourcing to dynamic service environments. Cloud Federations envisage a distributed, heterogeneous environment consisting of various cloud infrastructures by aggregating different IaaS provider capabilities coming from both the commercial and the academic area. In this paper, we introduce a federated cloud management solution that operates the federation through utilizing cloud-brokers for various IaaS providers. In order to enable an enhanced provider selection and inter-cloud service executions, an integrated monitoring approach is proposed which is capable of measuring the availability and reliability of the provisioned services in different providers. To this end, a minimal metric monitoring service has been designed and used together with a service monitoring solution to measure cloud performance. The transparent and cost effective operation on commercial clouds and the capability to simultaneously monitor both private and public clouds were the major design goals of this integrated cloud monitoring approach. Finally, the evaluation of our proposed solution is presented on different private IaaS systems participating in federations.     

A price- and-time-slot-negotiation mechanism for Cloud service reservations

When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources.     

Smart elastic scheduling algorithm for virtual machine migration in cloud computing

The Journal of Supercomputing - Cloud Computing has the facility to transform a large part of information technology into services in which computer resources are virtualized and made available as...     

Agent-based game-theoretic model for collaborative web services: Decision making analysis

In agent and (web) service computing, collaboration takes place when distributed entities have limited knowledge and capabilities, so they cannot perform required tasks without interacting and helping each other. For instance, web services, which are loosely-coupled business applications, are called to cooperate in distributed settings for the sake of efficiency. In this context, agents that abstract and act on behalf of web services could act in cooperative groups that gather a number of agents sharing some common goals. Enabling those agent-based web services to decide about their strategies in terms of joining and acting within groups, inviting other agents to join, and leaving a group to act alone is an open issue that we address in this work. In this paper, we propose a framework where agent-based web services select strategies that maximize their outcomes. These strategies could be categorized into cooperative strategies involving other agents and strategies that highlight the single operative attitude. Although cooperation seems to bring better utility to cooperative agents, we highlight that web services in some environments obtain better outcome while they act individually (i.e., outside the group). This means that the cost of cooperation (in some particular cases) might negatively influence the outcome and obtained utility. As solution, we propose in this paper (1) an agent-based model that formalizes web services decision making where different parameters are considered; and (2) a game-theoretic framework that analyzes the web services strategies allowing them to maximize their acting performance where non-zero-sum games are being used. The paper presents theoretical results, which are also confirmed through extensive simulations.     

A sharing-oriented service selection and scheduling approach for the optimization of resource utilization

We present a sharing-oriented service selection and scheduling approach capable of finding a trade-off between requirement satisfaction degree, service utilization rate and service sharing cost for limited quantities and capacities of available services. In traditional service selection approaches, each customer requirement is independently satisfied by optimally selecting a set of candidate service resources. However, in real-life service scenarios, it is usual for multiple customers to raise their requirements simultaneously, and available services need to be allocated between them. Especially, when available services are limited in both quantity and capacity, a traditional “first-come-first-serve” strategy would lead to a low service utilization rate, and some requirements cannot be satisfied at all (i.e., a low requirement satisfaction degree). Our approach makes use of the feature that some services can be shared by several customer requirements. Specifically, a virtualized service resource consisting of multiple candidate services is constructed and scheduled to satisfy multiple customer requirements simultaneously. Our approach searches for the global optimization on requirement satisfaction degree, service utilization rate, and service sharing cost. We build a mathematical model for this multi-objective optimization problem and propose a nested genetic algorithm mixed with a greedy strategy. Experiments in an ocean transportation service setting are conducted and our approach is compared with traditional approaches to validate its effectiveness.     

基于SDN/NFV的安全服务链自动编排部署框架

针对云计算等虚拟化环境的安全防护问题,提出了一种基于SDN/NFV技术思想的安全服务链自动编排部署框架.论文通过扩展ABAC策略模型以描述用户的安全需求,采用优先级解决策略冲突以编排虚拟安全设备,依据网络中虚拟安全设备实例负载与实时链路传输时延来调度网络流,最终由SDN控制器生成流表下发到网络中完成流量重定向,实现了根据安全需求自动构建安全服务链的过程.整个框架在基于开源控制器FloodLight和虚拟安全设备的实验环境中实现了自动编排部署,取得了预期效果.     

Supply web co-ordination by an agent-based trading network with integrated logistics services

B2B interactions, like electronic negotiations and auctions between suppliers and customers, could be significantly improved by enabling the participants to adapt their bidding strategies to current logistics information (e.g., about transportation condition, cost or dates) while the negotiation goes on. We present an approach of an agent-based information and trading network (ITN) called CASA for dynamic production and sales of timber; the integrated services for logistics and e-commerce are efficiently coordinated by appropriate types of holonic structured intelligent agents of the network. We introduce the agent-based architecture and describe how the agents build their plans and optimize them afterwards. For optimizing their plans, the agents use various market-based negotiation mechanisms, i.e., several auction mechanisms and the simulated trading mechanism described in detail in this article. The effects of the different mechanisms on resulting cost and surplus have been evaluated by various simulation runs in different competitive and co-operative settings. It turned out that the simulated trading mechanism as well as matrix auction mechanisms for two or three items are especially suitable for supply web co-ordination and optimization tasks.     

Reliable and efficient big service selection

Big services, both virtual (e.g., cloud services) and physical (e.g., public transportation), are evolving rapidly to handle and deal with big data. By aggregating services from various domains, big services adopt selection schemes to produce composite service solutions that meet customer requirements. However, unlike traditional service selection, a huge number of big services require some lengthy selection processes to improve the service reliability. In this paper, we propose an efficient big service selection approach based on the coefficient of variation and mixed integer programming that improves the solution in two senses: 1) minimizing the time cost and 2) maximizing the reliability. We tested our approach on real-world datasets, and the experimental results indicated that our approach is superior to others.