Integrated Green Cloud Computing Architecture

Arbitrary usage of cloud computing, either private or public, can lead to uneconomical energy consumption in data processing, storage and communication. Hence, green cloud computing solutions aim not only to save energy but also reduce operational costs and carbon footprints on the environment. In this paper, an Integrated Green Cloud Architecture (IGCA) is proposed that comprises of a client-oriented Green Cloud Middleware to assist managers in better overseeing and configuring their overall access to cloud services in the greenest or most energy-efficient way. Decision making, whether to use local machine processing, private or public clouds, is smartly handled by the middleware using predefined system specifications such as service level agreement (SLA), Quality of service (QoS), equipment specifications and job description provided by IT department. Analytical model is used to show the feasibility to achieve efficient energy consumption while choosing between local, private and public Cloud service provider (CSP).Comment: 6 pages, International Conference on Advanced Computer Science Applications and Technologies, ACSAT 201

Energy Efficient Service Delivery in Clouds in Compliance with the Kyoto Protocol

Cloud computing is revolutionizing the ICT landscape by providing scalable and efficient computing resources on demand. The ICT industry - especially data centers, are responsible for considerable amounts of CO2 emissions and will very soon be faced with legislative restrictions, such as the Kyoto protocol, defining caps at different organizational levels (country, industry branch etc.) A lot has been done around energy efficient data centers, yet there is very little work done in defining flexible models considering CO2. In this paper we present a first attempt of modeling data centers in compliance with the Kyoto protocol. We discuss a novel approach for trading credits for emission reductions across data centers to comply with their constraints. CO2 caps can be integrated with Service Level Agreements and juxtaposed to other computing commodities (e.g. computational power, storage), setting a foundation for implementing next-generation schedulers and pricing models that support Kyoto-compliant CO2 trading schemes

Energy Aware SLA with Classification of Jobs for Cloud Environment

AbstractThe accelerated growth of the cloud eco-system is leading to the progress of new services, innovative ideas for the service replen- ishing and the newest interaction models both among the cloud providers and the customers which take advantage of the cloud resources. SLAs are one of the factors which allow for different interactions by keeping the objectives over privacy,QoS attributes and security constraints driving towards QoP attributes, the description of actions is needed in order to deliver the services ac- cording to the QoS attributes as expected by the customers. Energy aware SLAs extends the existing SLA agreements in order to include energy and carbon aware parameters. In this paper we propose an approach in order to relax certain jobs in a standardized way to obtain high energy consumption without disturbing the efficiency and availability of the system especially during the peak load times. The results for the above proposal are being discussed in this paper and were able to find that it is energy efficient

A framework for orchestrating secure and dynamic access of IoT services in multi-cloud environments

IoT devices have complex requirements but their limitations in terms of storage, network, computing, data analytics, scalability and big data management require it to be used it with a technology like cloud computing. IoT backend with cloud computing can present new ways to offer services that are massively scalable, can be dynamically configured, and delivered on demand with largescale infrastructure resources. However, a single cloud infrastructure might be unable to deal with the increasing demand of cloud services in which hundreds of users might be accessing cloud resources, leading to a big data problem and the need for efficient frameworks to handle a large number of user requests for IoT services. These challenges require new functional elements and provisioning schemes. To this end, we propose the usage of multi-clouds with IoT which can optimize the user requirements by allowing them to choose best IoT services from many services hosted in various cloud platforms and provide them with more infrastructure and platform resources to meet their requirements. This paper presents a novel framework for dynamic and secure IoT services access across multi-clouds using cloud on-demand model. To facilitate multi-cloud collaboration, novel protocols are designed and implemented on cloud platforms. The various stages involved in the framework for allowing users access to IoT services in multi-clouds are service matchmaking (i.e. to choose the best service matching user requirements), authentication (i.e. a lightweight mechanism to authenticate users at runtime before granting them service access), and SLA management (including SLA negotiation, enforcement and monitoring). SLA management offers benefits like negotiating required service parameters, enforcing mechanisms to ensure that service execution in the external cloud is according to the agreed SLAs and monitoring to verify that the cloud provider complies with those SLAs. The detailed system design to establish secure multi-cloud collaboration has been presented. Moreover, the designed protocols are empirically implemented on two different clouds including OpenStack and Amazon AWS. Experiments indicate that proposed system is scalable, authentication protocols result only in a limited overhead compared to standard authentication protocols, and any SLA violation by a cloud provider could be recorded and reported back to the user.N/

A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce

Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that model the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers