Load Balancing and Virtual Machine Allocation in Cloud-based Data Centers

As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of Virtual Machines has been simulated on CloudSim – a Java based cloud simulator

VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

User subscription-based resource management for Desktop-as-a-Service platforms

The Desktop-as-a-Service (DaaS) idiom consists of utilizing a cloud or other server infrastructure to host the user's desktop environment as a virtual desktop. Typical for cloud and DaaS services is the pay-as-you-go pricing model in combination with the availability of multiple subscription types to accommodate the needs of the users. However, optimal cost-efficient allocation of the virtual desktops to the infrastructure proves to be a combinatorial NP-hard problem, for which a heuristic is presented in the current article. We present a cost model for the DaaS service, from which a revenue of different configurations of virtual desktops to the servers can be derived. In this cost model, both subscription fee and penalties for degraded service are recorded, that are described in service-level agreements (SLAs) between the service provider and the users, and make realistic assumptions that different subscription types result in particular SLA contracts. The heuristic proposed states that for a given user base for which the virtual desktops (VDs) must be hosted, the VDs should be spread evenly over the infrastructure. Experiments through discrete event simulation show that this heuristic yields an approximation within 1 % of the theoretically achievable revenue

A Secure and Fair Resource Sharing Model for Community Clouds

Cloud computing has gained a lot of importance and has been one of the most discussed segment of today\u27s IT industry. As enterprises explore the idea of using clouds, concerns have emerged related to cloud security and standardization. This thesis explores whether the Community Cloud Deployment Model can provide solutions to some of the concerns associated with cloud computing. A secure framework based on trust negotiations for resource sharing within the community is developed as a means to provide standardization and security while building trust during resource sharing within the community. Additionally, a model for fair sharing of resources is developed which makes the resource availability and usage transparent to the community so that members can make informed decisions about their own resource requirements based on the resource usage and availability within the community. Furthermore, the fair-share model discusses methods that can be employed to address situations when the demand for a resource is higher than the resource availability in the resource pool. Various methods that include reduction in the requested amount of resource, early release of the resources and taxing members have been studied, Based on comparisons of these methods along with the advantages and disadvantages of each model outlined, a hybrid method that only taxes members for unused resources is developed. All these methods have been studied through simulations

Efficient cloud computing system operation strategies

Cloud computing systems have emerged as a new paradigm of computing systems by providing on demand based services which utilize large size computing resources. Service providers offer Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) to users depending on their demand and users pay only for the user resources. The Cloud system has become a successful business model and is expanding its scope through collaboration with various applications such as big data processing, Internet of Things (IoT), robotics, and 5G networks. Cloud computing systems are composed of large numbers of computing, network, and storage devices across the geographically distributed area and multiple tenants employ the cloud systems simultaneously with heterogeneous resource requirements. Thus, efficient operation of cloud computing systems is extremely difficult for service providers. In order to maximize service providers\u27 profit, the cloud systems should be able to serve large numbers of tenants while minimizing the OPerational EXpenditure (OPEX). For serving as many tenants as possible tenants using limited resources, the service providers should implement efficient resource allocation for users\u27 requirements. At the same time, cloud infrastructure consumes a significant amount of energy. According to recent disclosures, Google data centers consumed nearly 300 million watts and Facebook\u27s data centers consumed 60 million watts. Explosive traffic demand for data centers will keep increasing because of expansion of mobile and cloud traffic requirements. If service providers do not develop efficient ways for energy management in their infrastructures, this will cause significant power consumption in running their cloud infrastructures. In this thesis, we consider optimal datasets allocation in distributed cloud computing systems. Our objective is to minimize processing time and cost. Processing time includes virtual machine processing time, communication time, and data transfer time. In distributed Cloud systems, communication time and data transfer time are important component of processing time because data centers are distributed geographically. If we place data sets far from each other, this increases the communication and data transfer time. The cost objective includes virtual machine cost, communication cost, and data transfer cost. Cloud service providers charge for virtual machine usage according to usage time of virtual machine. Communication cost and transfer cost are charged based on transmission speed of data and data set size. The problem of allocating data sets to VMs in distributed heterogeneous clouds is formulated as a linear programming model with two objectives: the cost and processing time. After finding optimal solutions of each objective function, we use a heuristic approach to find the Pareto front of multi-objective linear programming problem. In the simulation experiment, we consider a heterogeneous cloud infrastructure with five different types of cloud service provider resource information, and we optimize data set placement by guaranteeing Pareto optimality of the solutions. Also, this thesis proposes an adaptive data center activation model that consolidates adaptive activation of switches and hosts simultaneously integrated with a statistical request prediction algorithm. The learning algorithm predicts user requests in predetermined interval by using a cyclic window learning algorithm. Then the data center activates an optimal number of switches and hosts in order to minimize power consumption that is based on prediction. We designed an adaptive data center activation model by using a cognitive cycle composed of three steps: data collection, prediction, and activation. In the request prediction step, the prediction algorithm forecasts a Poisson distribution parameter lambda in every determined interval by using Maximum Likelihood Estimation (MLE) and Local Linear Regression (LLR) methods. Then, adaptive activation of the data center is implemented with the predicted parameter in every interval. The adaptive activation model is formulated as a Mixed Integer Linear Programming (MILP) model. Switches and hosts are modeled as M/M/1 and M/M/c queues. In order to minimize power consumption of data centers, the model minimizes the number of activated switches, hosts, and memory modules while guaranteeing Quality of Service (QoS). Since the problem is NP-hard, we use the Simulated Annealing algorithm to solve the model. We employ Google cluster trace data to simulate our prediction model. Then, the predicted data is employed to test adaptive activation model and observed energy saving rate in every interval. In the experiment, we could observe that the adaptive activation model saves 30 to 50% of energy compared to the full operation state of data center in practical utilization rates of data centers. Network Function Virtualization (NFV) emerged as a game changer in network market for efficient operation of the network infrastructure. Since NFV transforms the dedicated physical devices designed for specific network function to software-based Virtual Machines (VMs), the network operators expect to reduce a significant Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). Softwarized VMs can be implemented on any commodity servers, so network operators can design flexible and scalable network architecture through efficient VM placement and migration algorithms. In this thesis, we study a joint problem of Virtualized Network Function (VNF) resource allocation and NFV-Service Chain (NFV-SC) placement problem in Software Defined Network (SDN) based hyper-scale distributed cloud computing infrastructure. The objective of the problem is minimizing the power consumption of the infrastructure while enforcing Service Level Agreement (SLA) of users. We employ an M/G/1/K queuing network approximation analysis for the NFV-SC model. The communication time between VNFs is considered in the NFV-SC placement because it influences the performance of NFV-SC in the highly distributed infrastructure environment. The joint problem is modeled by a Mixed Integer Non-linear Programming (MINP) model. However, the problem is intractable in large size infrastructures due to NP-hardness of the problem. We therefore propose a heuristic algorithm which splits the problem into two sub-problems: resource allocation and the NFV-SC embedding. In the numerical analysis, we could observe that the proposed algorithm outperforms the traditional bin packing algorithms in terms of power consumption and SLA assurance. In this thesis, we propose efficient cloud infrastructure management strategies from a single data center point of view to hyper-scale distributed cloud computing infrastructure for profitable cloud system operation. The management schemes are proposed with various objectives such as Quality of Service (Qos), performance, latency, and power consumption. We use efficient mathematical modeling strategies such as Linear Programming (LP), Mixed Integer Linear Programming (MILP), Mixed Integer Non-linear Programming(MINP), convex programming, queuing theory, and probabilistic modeling strategies and prove the efficiency of the proposed strategies through various simulations