Optimized Contract-based Model for Resource Allocation in Federated Geo-distributed Clouds

In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go modelcontinues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scaleand large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises newchallenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resourcestowards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achievingglobal efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies inlocal resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profitearned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPsto establish resource sharing contracts with individual datacentersapriorifor defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model

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

Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

Load Balancing Algoritms in Cloud Computing Environment: A Review

Cloud computing is an emerging internet based technology. Cloud is a platform providing pool of resources and virtualization. It is based on pay-as-you-go model. The numbers of users accessing the cloud are rising day by day. Generally clouds are based on data centers which are powerful to handle large number of users. The reliability of clouds depends on the way it handle loads, to overcome such problems clouds must be featured with the load balancing mechanism. Load balancing is required as we don’t want one centralized severs performance to be degraded. A lot of algorithms have been proposed to do this task. In this paper we have studied the various existing load balancing algorithms and then compared them based on various parameters like resource utilization, scalability, stability etc