1. Introduction
since it uses request/reply paradigm. It does not suffer from single point of failure; if an FCM fails, the cloud provider can simply create a new FCM without any state transfer.
1.2.2 A Cost-aware Placement of Data-intensive Applications on Feder- ated Cloud
Research problem: How can a cloud provider minimize its TCO while allo- cating the resources for deploying a data-intensive application in a federated cloud system, taking into account the cost of energy consumption and network bandwidth?
In this work, a cloud provider tries to host data-intensive applications at minimum cost.
Continuous communication and a massive data transfer across the virtual components (VCs) of a data-intensive application, scheduled across the data centers adds to the communication cost. The communication cost must be minimized by allocating correlated VCs to the same data center. The energy consumption cost of the VCs should be minimized by leveraging electricity price and PUE variation across different data centers in the federation. Therefore, the problem at hand is, how to allocate groups of collaborating VCs to a set of data centers in a federation to minimize the TCO, including the costs of energy consumption and network bandwidth.
The main contributions of this work are:
1. We formulate the problem of placing collaborating VCs of a data-intensive application as an optimization problem to minimize the TCO, including costs of energy consump- tion and inter-data center communication, with a set of constraints that represent resource availability.
2. Using the hierarchical management framework, we propose an algorithm for resource allocation that divides the VCs into a set of clusters based on the communication requirements. The data-intensive application is represented as a weighted directed graph where the nodes are the virtual components, and the edges express their traffic demand. The proposed algorithm receives an application request and runs iteratively in two phases; Partitioning and Mapping. The Partitioning phase partitions the request graph into clusters based on the traffic density between the nodes by removing the lowest weighted edges, minimizing inter-data center communication and bandwidth
1.2. Contributions of the Thesis
cost. The Mapping phase allocates the created clusters of virtual components to a data center with a minimum energy cost (considering electricity price and PUE) to minimize the operating cost. We analyze the time and space complexity of the algorithm with respect to the number of VCs and the number of data centers, and find them to be polynomial. We find the cost of running the algorithm to be negligible compared to the cost saving achieved by the algorithm. The proposed algorithm is seen to outperform other baseline approaches in terms of energy cost, communication cost and the total cost.
1.2.3 Cost-and-Delay Aware Dynamic Resource Allocation in Federated Vehicular Clouds
Research problem: How can a cloud provider hosting vehicular services allocate resources dynamically in a federated cloud system to minimize the cost while assuring the QoS requirements of different vehicular services, considering the user mobility?
In this problem, we aim to minimize the cost of the cloud provider while considering the delay constraints of various applications. Typically, modern vehicular applications are delay-aware. Based on their delay requirements, they can be classified into two categories;
delay-sensitive (DS) and delay-tolerant (DT) applications, which are characterized by different delay thresholds and corresponding cost thresholds [54]. The cloud provider has to minimize the delay for DS applications and the delay should not exceed a threshold. In contrast, DT applications only require the delay not to exceed a reasonably large threshold.
For both applications, the associated cost thresholds should be considered during resource allocation. Further, user mobility in VCC makes the problem more challenging, where VM migration might be employed to meet the delay and the cost requirements continuously.
The main contributions of this work are:
1. We introduce Federated Vehicular Cloud Computing (FVCC) architecture which applies the federated cloud in VCC to handle the resource-intensive vehicular ap- plications. FVCC leverages the advantages of the federation for hosting vehicular applications, such as minimizing the cost and the delay.
2. We propose a dynamic pricing model for resource sharing among the cloud providers.
1. Introduction
This pricing model defines a VM instance’s renting price to other cloud providers in the federation, which ranges dynamically between the operating cost and the end-user price based on resource utilization.
3. We propose a cost-and-delay aware dynamic resource management algorithm in FVCC that classifies the user request based on application category (DS or DT) and performs online resource allocation considering the application’s requirements and the associated cost threshold. It also migrates the VMs when required (based on mobility and delay requirements). The proposed algorithm is shown to meet the delay requirements with a significant cost reduction to the provider up to 50% compared to baseline methods.
It serves more than 90% of requests with lower cost and fewer VM migrations.
1.2.4 Towards Cost-aware VM Migration to Maximize the Profit in Fed- erated Clouds
Research problem: How can a cloud provider utilize VM live migration across data centers in a federated cloud effectively to maximize its profit while mini- mizing the migration cost and time?
In this problem, we explore inter-data center live migration in a federated cloud system to maximize the profit of the cloud provider by minimizing its TCO through leveraging the variation in resource price across data centers of the federation. However, inter-data center migration adds extra cost due to data transfer of the migrated VMs. We attempt to minimize the TCO and the migration time at the same time. We also study the various parameters that affect the migration performance and cost, including the parameters related to VM (i.e., type, lifetime, PDR, and eligibility for migration) and the parameters related to data centers (bandwidth and data transfer price). We present a comprehensive model for estimating migration performance and cost considering all parameters. The main contributions of this work are:
1. We model the VM cost in a federated cloud system; based on the energy cost (if the VM is hosted locally) and using the dynamic pricing model proposed in the previous problem (if the VM is outsourced to another cloud provider). We also model the migration cost, migration time, and downtime of migration.