Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centers

In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA), Optimised Firefly Search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC

A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

Managing cloud datacenters is the most prevailing challenging task ahead for the IT industries. The data centers are considered to be the main source for resource provisioning to the cloud users. Managing these resources to handle large number of virtual machine requests has created the need for heuristic optimization algorithms to provide the optimal placement strategies satisfying the objectives and constraints formulated. In this paper, we propose to apply firefly colony and fuzzy firefly colony optimization algorithms to solve two key issues of datacenters, namely, server consolidation and multiobjective virtual machine placement problem. The server consolidation aims to minimize the count of physical machines used and the virtual machine placement problem is to obtain optimal placement strategy with both minimum power consumption and resource wastage. The proposed techniques exhibit better performance than the heuristics and metaheuristic approaches considered in terms of server consolidation and finding optimal placement strategy

On-Demand VM Placement on Cloud Infrastructure

Cloud Computing paradigm is most popular because of its exibility for provisioning resources quickly and eciently. In cloud computing the resource requests are served by creating virtual machines of the requested specication on the underlying physical infrastructure. If the placement of virtual machines to the underlying physical machines will take long time or if all the accepted virtual machine requests can't be served then some exibility will lost. In on-demand access to cloud computing services the requested resources are served on the available infrastructure for short span of time. In on-demand access the number of resource requests in a particular time interval can not be predicted unlike in case of spot-market access. As a virtual machine instance will run on a single physical machine at a time, hence to serve more requests in case of on-demand access we have to use the available resource optimally considering the allocation cost and SLA violation. In this work we tried to improve the resource utilization by considering single dimensional best strategy, which not only reduce the cost by utilizing minimum number of resources but also minimize the SLA violation which may arise due to failure in allocating all the requested virtual machine. We have developed a framework that optimizes the use of physical infrastructure by eectively allocating the requested virtual machines and also reduces the allocation time. The proposed allocation policy is compared with three other existing policies named Greedy First Fit, Ranking and Round-Robin, by simulating all policies using CloudSim toolkit and the performance is evaluated by considering various parameters

Carbon-profit-aware job scheduling and load balancing in geographically distributed cloud for HPC and web applications

This thesis introduces two carbon-profit-aware control mechanisms that can be used to improve performance of job scheduling and load balancing in an interconnected system of geographically distributed data centers for HPC and web applications. These control mechanisms consist of three primary components that perform: 1) measurement and modeling, 2) job planning, and 3) plan execution. The measurement and modeling component provide information on energy consumption and carbon footprint as well as utilization, weather, and pricing information. The job planning component uses this information to suggest the best arrangement of applications as a possible configuration to the plan execution component to perform it on the system. For reporting and decision making purposes, some metrics need to be modeled based on directly measured inputs. There are two challenges in accurately modeling of these necessary metrics: 1) feature selection and 2) curve fitting (regression). First, to improve the accuracy of power consumption models of the underutilized servers, advanced fitting methodologies were used on the selected server features. The resulting model is then evaluated on real servers and is used as part of load balancing mechanism for web applications. We also provide an inclusive model for cooling system in data centers to optimize the power consumption of cooling system, which in turn is used by the planning component. Furthermore, we introduce another model to calculate the profit of the system based on the price of electricity, carbon tax, operational costs, sales tax, and corporation taxes. This model is used for optimized scheduling of HPC jobs. For position allocation of web applications, a new heuristic algorithm is introduced for load balancing of virtual machines in a geographically distributed system in order to improve its carbon awareness. This new heuristic algorithm is based on genetic algorithm and is specifically tailored for optimization problems of interconnected system of distributed data centers. A simple version of this heuristic algorithm has been implemented in the GSN project, as a carbon-aware controller. Similarly, for scheduling of HPC jobs on servers, two new metrics are introduced: 1) profitper-core-hour-GHz and 2) virtual carbon tax. In the HPC job scheduler, these new metrics are used to maximize profit and minimize the carbon footprint of the system, respectively. Once the application execution plan is determined, plan execution component will attempt to implement it on the system. Plan execution component immediately uses the hypervisors on physical servers to create, remove, and migrate virtual machines. It also executes and controls the HPC jobs or web applications on the virtual machines. For validating systems designed using the proposed modeling and planning components, a simulation platform using real system data was developed, and new methodologies were compared with the state-of-the-art methods considering various scenarios. The experimental results show improvement in power modeling of servers, significant carbon reduction in load balancing of web applications, and significant profit-carbon improvement in HPC job scheduling

Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool. The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction

Automated hierarchical service level agreements

The present dissertation concerns the area of Service Computing. More specifically, it contributes to the topic of enabling IT service stacks with dependability, such that they can be used even further in pragmatic business environments and applications. The instrument used for this purpose is a Service Level Agreement (SLA). The main focus is on SLA Hierarchies, which reflect corresponding Service Hierarchies. SLAs may be established manually, or automatically among software agents; it is mainly the latter case that is considered here. The thesis contributes by means of a formal problem definition for the construction of SLA hierarchies using a translation process, a management architecture, a formal model for defining penalties and a representation that facilitates the processing of SLAs. Using these tools, it is shown that automated SLA management in hierarchical setups is possible, through an application to Multi-Domain Infrastructure-as-a-Service. Within this specific technical area, different SLA-based resource capacity planning approaches are examined via simulation -- both for online and offline planning. The former case concerns normal runtime operations, and the thesis examines two greedy algorithms with regard to their energy-savings efficiency and their performance. In the latter case, a resource-scarce environment is simulated with the purpose of minimizing penalties from already established SLAs. This is achieved via formally-defined combinatorial models, which are solved and compared to two greedy algorithms