Tag: Cloud Computing

Accepted paper for presentation in GECON 2012

An Economic Agent Maximizing Cloud Provider Revenues Under a Pay-as-you-Book Pricing Model by Felipe Díaz Sánchez, Elias A. Doumith, Sawsan Al Zahr, and Maurice Gagnaire.

Abstract: The Cloud computing paradigm offers the illusion of infinite resources accessible to end-users anywhere at anytime. In such dynamic environment, managing distributed heterogenous resources is challenging. A Cloud workload is typically decomposed into advance reservation and on-demand requests. Under advance reservation, end-users have the opportunity to reserve in advance the estimated required resources for the completion of their jobs without any further commitment. Thus, Cloud service providers can make a better use of their infrastructure while provisioning the proposed services under determined policies and/or time constraints. However, estimating end-users resource requirements is often error prone. Such uncertainties associated with job execution time and/or SLA satisfaction significantly increase the complexity of the resource management.

Accepted paper for publication in Future Generation Computer Systems Journal

Towards an Optimized Abstracted Topology Design in Cloud Environment by Rosy Aoun, Chinwe E. Abosi, Elias A. Doumith, Reza Nejabati, Maurice Gagnaire, and Dimitra Simeonidou.

Abstract: The rapid development and diversification of Cloud services occurs in a very competitive environment. The number of actors providing Infrastructure as a Service (IaaS) remains limited, while the number of PaaS (Platform as a Service) and SaaS (Software as a Service) providers is rapidly increasing. In this context, the ubiquity and the variety of Cloud services impose a form of collaboration between all these actors. For this reason, Cloud Service Providers (CSPs) rely on the availability of computing, storage, and network resources generally provided by various administrative entities. This multi-tenant environment raises multiple challenges such as confidentiality and scalability issues. To address these challenges, resource (network, computing, and storage) abstraction is introduced. In this paper, we focus on network resource abstraction algorithms used by a Network Service Provider (NSP) for sharing its network topology without exposing details of its physical resources. In this context, we propose two network resource abstraction techniques. Firstly, we formulate the network topology abstraction problem as a Mixed-Integer Linear Program (MILP). Solving this formulation provides an optimal abstracted topology to the CSP in terms of availability of the underlying resources. Secondly, we propose an innovative scalable algorithm called SILK–ALT inspired from the SImple LinK (SILK) algorithm previously proposed by Abosi et al. We compare the MILP formulation, the SILK–ALT algorithm, and the SILK algorithm in terms of rejection ratio of users requests at both the Cloud provider and the network provider levels. Using our proposed algorithms, the obtained numerical results show that resource abstraction in general and network topology abstraction in particular can effectively hide details of the underlying infrastructure. Moreover, these algorithms represent a scalable and sufficiently accurate way of advertising the resources in a multi-tenant environment.

Accepted paper for presentation in Cloudcom 2011

Dynamic Resource Allocation in Cloud Environment Under Time-variant Job Requests by Davide Tammaro, Elias A. Doumith, Sawsan Al Zahr, Jean-Paul Smets, and Maurice Gagnaire.

Abstract: In a Cloud environment, efficient resource provisioning and management present today a challenging issue because of the dynamic nature of the Cloud on one hand, and the need to satisfy heterogeneous resource requirements on the other hand. In such a dynamic environment where end-users can arrive and leave the Cloud at any time, a Cloud service provider (CSP) should be able to make accurate decisions for scaling up or down its data-centers while taking into account several utility criteria, eg the delay of virtual resources setup, the migration of existing processes, the resource utilization, etc. In order to satisfy both parties (the CSP and the end-users), an efficient and dynamic resource allocation strategy is mandatory.
In this paper, we propose an original approach for dynamic resource allocation in a Cloud environment. Our proposal considers computing job requests that are characterized by their arrival and teardown times, as well as a predictive profile of their computing requirements during their activity period. Assuming a prior knowledge of the predicted computing resources required by end-users, we propose and investigate several algorithms with different optimization criteria. However, prediction errors may occur resulting in some cases in the drop of one or several computing requests. Our proposed algorithms are compared in terms of various performance parameters including the rejection ratio, the dropping ratio, as well as the satisfaction of the end-users and the CSP.

Accepted paper for presentation in Cloudcom 2011

Impact of Resource over-Reservation (ROR) and Dropping Policies on Cloud Resource Allocation by Felipe Dìaz, Elias A. Doumith, and Maurice Gagnaire.

Abstract: In Cloud environment, Cloud Providers (CP) grant access to computing and storage resources through Web-portals. Resource virtualization is a key enabler for providing such services. Thanks to virtualization, multiple Virtual Machines (VMs) can be hosted by the same Physical Machine (PM). Existing CPs, such as Amazon Web Services and Windows Azure, offer pre-configured VMs with fixed-size computing, storage and network capacities. In this context, end-users can only choose from a set of predefined VM instances offered by the CPs. However, it is expected that, in the near-future, end-users will be able to access the Cloud without any restriction on the size of the required resources and thus, will be charged according to the amount of resources used. In such scenario, the major problem faced by a CP is to select the appropriate PM that will host a new VM while still satisfying the end-user requirements. This resource allocation problem is similar to the well-known online Bin Packing problem. In this paper, we investigate several algorithms that were proposed to solve the Bin Packing problem, and compare them in a Cloud environment in terms of resource utilization and percentage of dropped VMs. The novel concept of Resource Over-Reservation (ROR) is introduced as a mean to reduce the percentage of dropped VMs and to improve resource utilization.

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