Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Energy-Aware Cloud Management through Progressive SLA Specification

Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed VM may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply such energy-aware management. In this paper we show how to analyse past traces of dynamic cloud management actions based on electricity prices and temperatures to estimate VM availability and price values. We propose a novel SLA specification approach for offering VMs with different availability and price values guaranteed over multiple SLAs to enable flexible energy-aware cloud management. We determine the optimal number of such SLAs as well as their availability and price guaranteed values. We evaluate our approach in a user SLA selection simulation using Wikipedia and Grid'5000 workloads. The results show higher customer conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Research Opportunities in an Intercloud Environment Using MOSt in SLA4CLOUD Project

International audienceActually, Internet services are becoming essential for different types of users. This evolution impacts how data connections , network routes and resources are configured and used. In this context, the way in which distributed applications and services is becoming more difficult to manage. Cloud computing allows interactions between cloud providers and cloud service providers, and cloud providers can offer deployment services in different datacenters located in different world regions. Much development effort is needed for deploying scalable solutions. One of the these challenges is how to design, develop and deploy cloud solutions that could meet the policies and security requirements of multiple environments needs. The SLA4CLOUD project intends to build an environment where a user can request the deployment of its services anywhere in the underlying infrastructure, using the MOSt platform and its services. This work aims to report some opportunities and research challenges resulting from SLA4CLOUD project in the context of MOSt platform, and the promotion of new projects and partnerships

Client Classification and Dynamic Pricing of Resources in Cloud using Cloud Simulator

In cloud computing scenarios, the resources like hardware or software or applications can be accessed whenever a client needs. The client needs to get a subscription to avail service. The usages are monitored by service provider for each client over a network. When more number of client issues a request in cloud, scheduling the resources and pricing of taken resources from the cloud have a huge difference on the incomes. The job request from the user is  scheduled in cloud is done, based on the need of the client (i.e) client classification algorithm and based on the demand pricing is fixed (i.e) dynamic pricing scheme. These algorithms solves income optimization problem by allocating resources under a given price and pricing of the resources are based on the allocation. The experiments were proposed with cloud simulation. Cloud simulations results are measured with the first come first serve, client classification and dynamic pricing model. The proposed algorithm shows the better resource allocation based on the pricing method

Cloud Market Maker: An automated dynamic pricing marketplace for cloud users

© 2015 Elsevier B.V. Abstract Cloud providers commonly incur heavy upfront set up costs which remain almost constant whether they serve a single or many customers. In order to generate a return on this investment, a suitable pricing strategy is required by providers. Established industries such as the airlines employ dynamic pricing to maximize their revenues. In order to increase their resource utilization rates, cloud providers could also use dynamic pricing for their services. At present however most providers use static schemes for pricing their resources. This work presents a new dynamic pricing mechanism for cloud providers. Furthermore, at present no platform exists that provides a dynamic unified view of the different cloud offerings in real-time. Due to a rapidly changing landscape and a limited knowledge of the cloud marketplace, consumers can often end up choosing a cloud provider that is more expensive or does not give them what they really need. This is because some providers spend significantly on advertising their services online. In order to assist cloud customers in the selection of a suitable resource and cloud providers in implementing dynamic pricing, this work describes an automated dynamic pricing marketplace and a decision support system for cloud users. We present a multi-agent multi-auction based system through which such services are delivered. An evaluation has been carried out to determine how effectively the Cloud Market Maker selects the resource, dynamically adjusts the price for the cloud users and the suitability of dynamic pricing for the cloud environment