Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos

Multi-layer virtual transport network design

Service overlay networks and network virtualization enable multiple overlay/virtual networks to run over a common physical network infrastructure. They are widely used to overcome deficiencies of the Internet (e.g., resiliency, security and QoS guarantees). However, most overlay/virtual networks are used for routing/tunneling purposes, and not for providing scoped transport flows (involving all mechanisms such as error and flow control, resource allocation, etc.), which can allow better network resource allocation and utilization. Most importantly, the design of overlay/virtual networks is mostly single-layered, and lacks dynamic scope management, which is important for application and network management. In response to these limitations, we propose a multi-layer approach to Virtual Transport Network (VTN) design. This design is a key part of VTN-based network management, where network management is done via managing various VTNs over different scopes (i.e., ranges of operation). Our simulation and experimental results show that our multi-layer approach to VTN design can achieve better performance compared to the traditional single-layer design used for overlay/virtual networks.This work has been partly supported by National Science Foundation awards: CNS-0963974 and CNS-1346688

An empirical study of memory sharing in virtual machines

Content-based page sharing is a technique often used in virtualized environments to reduce server memory requirements. Many systems have been proposed to capture the benefits of page sharing. However, there have been few analyses of page sharing in general, both considering its real-world utility and typical sources of sharing potential. We provide insight into this issue through an exploration and analysis of memory traces captured from real user machines and controlled virtual machines. First, we observe that absolute sharing levels (excluding zero pages) generally remain under 15%, contrasting with prior work that has often reported savings of 30% or more. Second, we find that sharing within individual machines often accounts for nearly all (\u3e90%) of the sharing potential within a set of machines, with inter-machine sharing contributing only a small amount. Moreover, even small differences between machines significantly reduce what little inter-machine sharing might otherwise be possible. Third, we find that OS features like address space layout randomization can further diminish sharing potential. These findings both temper expectations of real-world sharing gains and suggest that sharing efforts may be equally effective if employed within the operating system of a single machine, rather than exclusively targeting groups of virtual machines

Overcommitment in Cloud Services -- Bin packing with Chance Constraints

This paper considers a traditional problem of resource allocation, scheduling jobs on machines. One such recent application is cloud computing, where jobs arrive in an online fashion with capacity requirements and need to be immediately scheduled on physical machines in data centers. It is often observed that the requested capacities are not fully utilized, hence offering an opportunity to employ an overcommitment policy, i.e., selling resources beyond capacity. Setting the right overcommitment level can induce a significant cost reduction for the cloud provider, while only inducing a very low risk of violating capacity constraints. We introduce and study a model that quantifies the value of overcommitment by modeling the problem as a bin packing with chance constraints. We then propose an alternative formulation that transforms each chance constraint into a submodular function. We show that our model captures the risk pooling effect and can guide scheduling and overcommitment decisions. We also develop a family of online algorithms that are intuitive, easy to implement and provide a constant factor guarantee from optimal. Finally, we calibrate our model using realistic workload data, and test our approach in a practical setting. Our analysis and experiments illustrate the benefit of overcommitment in cloud services, and suggest a cost reduction of 1.5% to 17% depending on the provider's risk tolerance

Multi-layer virtual transport network design and management

Nowadays there is an increasing need for a general paradigm that can simplify network management and further enable network innovations. Software Defined Networking (SDN) is an efficient way to make the network programmable and reduce management complexity, however it is plagued with limitations inherited from the legacy Internet (TCP/IP) architecture. On the other hand, service overlay networks and virtual networks are widely used to overcome deficiencies of the Internet. However, most overlay/virtual networks are single-layered and lack dynamic scope management. Furthermore, how to solve the joint problem of designing and mapping the overlay/virtual network requests for better application and network performance remains an understudied area. In this thesis, in response to limitations of current SDN management solutions and of the traditional single-layer overlay/virtual network design, we propose a recursive approach to enterprise network management, where network management is done through managing various Virtual Transport Networks (VTNs) over different scopes (i.e., regions of operation). Different from the traditional overlay/virtual network model which mainly focuses on routing/tunneling, our VTN approach provides communication service with explicit Quality-of-Service (QoS) support for applications via transport flows, i.e., it involves all mechanisms (e.g., addressing, routing, error and flow control, resource allocation) needed to meet application requirements. Our approach inherently provides a multi-layer solution for overlay/virtual network design. The contributions of this thesis are threefold: (1) we propose a novel VTN-based management approach to enterprise network management; (2) we develop a framework for multi-layer VTN design and instantiate it to meet specific application and network goals; and (3) we design and prototype a VTN-based management architecture. Our simulation and experimental results demonstrate the flexibility of our VTN-based management approach and its performance advantages