Physical Host Machines Research Articles

An Efficient ECK-Secured FCM-Based Firefly Optimization Algorithm for Dynamic Resource Sharing in Multi-Tenant SaaS Service Clouds

Multi-tenancy in cloud computing is one of the foremost approaches to share one application instance among different customers and it is generally used by Software as a service (SaaS) providers. The main objective of the proposed work is to minimize the down time of virtual machines essential for resource provisioning using cluster based secure dynamic resource sharing. The proposed secure dynamic resource sharing approach allocates the service tenants to matched Virtual Machines(VMs) and allocates the VMs into physical host machines using the elliptic curve key based firefly optimization approach. First the functional characteristics of service users are grouped into clusters using FCM (Fuzzy C_means clustering) algorithm as tenants. After clustering, the tenant users are checked for authorization with the help of elliptic curve key value. When the users in the tenants are authorized then the grouped services are scheduled dynamically using the firefly optimization algorithm. The result of the work is appraised in terms of resource utilization, execution time, speed, and speedup.

ESBL: Design and Implement A Cloud Integrated Framework for IoT Load Balancing

The continuous growth in wireless communication, the demand for sophisticated, simple and low-cost solutions are also increasing. The demand motivated the researchers to indulge into inventing suitable network solutions ranging from wireless sensor networks to wireless ad-hoc networks to Internet of Things (IoT). With the inventions coming from the researchers, the demand for further improvements into the existing researchers have also growth upbound. Initially the network protocols were the demand for research and further improvements. Nevertheless, the IoT devices are started getting used in various fields and started gathering a huge volume of data using complex application. This invites the demands for research on load balancing for IoT networks. Several research attempts were made to overcome the communication overheads caused by the heavy loads on the IoT networks. Theses research attempts proposed to manage the loads in the network by equally distributing the loads among the IoT nodes. Nonetheless, in the due course of time, the practitioners have decided to move the data collected by the IoT nodes and the applications processing those data in to the cloud. Hence, the challenge is to build an algorithm for cloud-based load balancer matching with the demands from the IoT network protocols. Hence, this work proposes a novel algorithm for managing the loads on cloud integrated IoT network frameworks. The proposed algorithm utilizes the analytics of loads on cloud computing environments driven by the physical host machines and the virtual environments. The major challenge addressed by this work is to design a load balancer considering the low availability of the energy and computational capabilities of IoT nodes but with the objective to improve the response time of the IoT network. The proposed algorithm for load balancer is designed considering the low effort integrations with existing IoT framework for making the wireless communication world a better place.

Virtual machine placement for minimizing connection cost in data center networks

We consider an optimization problem for finding efficient placement of virtual machines (VMs) in a data center network. In this problem, we receive requests of VMs from customers, and seek to determine those physical machines in the network that host the requested VMs under capacity constraints. The objective of the problem is to minimize the total connection cost of the VM placement. We propose two models of the connection cost, called the centralized and the distributed models. In the former model, the connection cost for each request is defined as the minimum length of networks connecting all physical host machines and a specified root node, while the network does not connect the root node in the latter model. We present approximation algorithms for this optimization problem. For the centralized model, we present an O(logθ)-approximation algorithm, where θ is the ratio of the largest to the smallest requests. For the distributed model with uniform requests, we present an O(logn)-approximation algorithm for networks on n nodes. We also present a heuristic-based algorithm for the distributed model with non-uniform requests, and verify the performance of our algorithms through computational experiments.

Dynamic resource demand prediction and allocation in multi‐tenant service clouds

SummaryCloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best‐fit heuristic approach. Performance results demonstrate how our best‐fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.

Asymptotic optimality of a greedy randomized algorithm in a large-scale service system with general packing constraints

We consider a service system model primarily motivated by the problem of efficient assignment of virtual machines to physical host machines in a network cloud, so that the number of occupied hosts is minimized.There are multiple types of arriving customers, where a customer’s mean service time depends on its type. There is an infinite number of servers. Multiple customers can be placed for service into one server, subject to general “packing” constraints. Service times of different customers are independent, even if served simultaneously by the same server. Each new arriving customer is placed for service immediately, either into a server already serving other customers (as long as packing constraints are not violated) or into an idle server. After a service completion, each customer leaves its server and the system. We propose an extremely simple and easily implementable customer placement algorithm, called Greedy-Random (GRAND). It places each arriving customer uniformly at random into either one of the already occupied servers (subject to packing constraints) or one of the so-called zero-servers, which are empty servers designated to be available to new arrivals. One instance of GRAND, called GRAND( $$aZ$$ ), where $$a\ge 0$$ is a parameter, is such that the number of zero-servers at any given time $$t$$ is $$aZ(t)$$ , where $$Z(t)$$ is the current total number of customers in the system. We prove that GRAND( $$aZ$$ ) with $$a>0$$ is asymptotically optimal, as the customer arrival rates grow to infinity and $$a\rightarrow 0$$ , in the sense of minimizing the total number of occupied servers in steady state. In addition, we study by simulations various versions of GRAND and observe the dependence of convergence speed and steady-state performance on the number of zero-servers.

An Infinite Server System with General Packing Constraints

We consider a service system model primarily motivated by the problem of efficient assignment of virtual machines to physical host machines in a network cloud, so that the number of occupied hosts is minimized. There are multiple input flows of different type customers, with a customer mean service time depending on its type. There is an infinite number of servers. A server-packing configuration is the vector k = {ki}, where ki is the number of type i customers the server “contains.” Packing constraints must be observed; namely, there is a fixed finite set of configurations k that are allowed. Service times of different customers are independent; after a service completion, each customer leaves its server and the system. Each new arriving customer is placed for service immediately; it can be placed into a server already serving other customers (as long as packing constraints are not violated), or into an idle server. We consider a simple parsimonious real-time algorithm, called Greedy, that attempts to minimize the increment of the objective function [Formula: see text], α > 0, caused by each new assignment; here Xk is the number of servers in configuration k. (When α is small, [Formula: see text] approximates the total number [Formula: see text] of occupied servers.) Our main results show that certain versions of the Greedy algorithm are asymptotically optimal, in the sense of minimizing [Formula: see text] in stationary regime as the input flow rates grow to infinity. We also show that in the special case when the set of allowed configurations is determined by vector-packing constraints, the Greedy algorithm can work with aggregate configurations as opposed to exact configurations k, thus reducing computational complexity while preserving the asymptotic optimality.

A large-scale service system with packing constraints

We consider a large-scale service system model proposed in [14], which is motivated by the problem of efficient placement of virtual machines to physical host machines in a network cloud, so that the total number of occupied hosts is minimized. Customers of different types arrive to a system with an infinite number of servers. A server packing configuration is the vector k = {k i }, where k i is the number of type-i customers that the server "contains". Packing constraints are described by a fixed finite set of allowed configurations. Upon arrival, each customer is placed into a server immediately, subject to the packing constraints; the server can be idle or already serving other customers. After service completion, each customer leaves its server and the system. It was shown in [14] that a simple real-time algorithm, called Greedy, is asymptotically optimal in the sense of minimizing ∑ k X k 1+α in the stationary regime, as the customer arrival rates grow to infinity. (Here α > 0, and X k denotes the number of servers with configuration k.) In particular, when parameter α is small, and in the asymptotic regime where customer arrival rates grow to infinity, Greedy solves a problem approximating one of minimizing ∑ k X k , the number of occupied hosts. In this paper we introduce the algorithm called Greedy with sublinear Safety Stocks (GSS), and show that it asymptotically solves the exact problem of minimizing ∑ k X k . An important feature of the algorithm is that sublinear safety stocks of X k are created automatically - when and where necessary - without having to determine a priori where they are required. Moreover, we also provide a tight characterization of the rate of convergence to optimality under GSS. The GSS algorithm is as simple as Greedy, and uses no more system state information than Greedy does.

Performance Analysis of Network I/O Workloads in Virtualized Data Centers

Server consolidation and application consolidation through virtualization are key performance optimizations in cloud-based service delivery industry. In this paper, we argue that it is important for both cloud consumers and cloud providers to understand the various factors that may have significant impact on the performance of applications running in a virtualized cloud. This paper presents an extensive performance study of network I/O workloads in a virtualized cloud environment. We first show that current implementation of virtual machine monitor (VMM) does not provide sufficient performance isolation to guarantee the effectiveness of resource sharing across multiple virtual machine instances (VMs) running on a single physical host machine, especially when applications running on neighboring VMs are competing for computing and communication resources. Then we study a set of representative workloads in cloud-based data centers, which compete for either CPU or network I/O resources, and present the detailed analysis on different factors that can impact the throughput performance and resource sharing effectiveness. For example, we analyze the cost and the benefit of running idle VM instances on a physical host where some applications are hosted concurrently. We also present an in-depth discussion on the performance impact of colocating applications that compete for either CPU or network I/O resources. Finally, we analyze the impact of different CPU resource scheduling strategies and different workload rates on the performance of applications running on different VMs hosted by the same physical machine.

Virtual Machines And Networks - Installation, Performance, Study, Advantages And Virtualization Options

The interest in virtualization has been growing rapidly in the IT industry because of inherent benefits like better resource utilization and ease of system manageability. The experimentation and use of virtualization as well as the simultaneous deployment of virtual software are increasingly getting popular and in use by educational institutions for research and teaching. This paper stresses on the potential advantages associated with virtualization and the use of virtual machines for scenarios, which cannot be easily implemented and/or studied in a traditional academic network environment, but need to be explored and experimented by students to meet the raising needs and knowledge-base demanded by the IT industry. In this context, we discuss various aspects of virtualization - starting from the working principle of virtual machines, installation procedure for a virtual guest operating system on a physical host operating system, virtualization options and a performance study measuring the throughput obtained on a network of virtual machines and physical host machines. In addition, the paper extensively evaluates the use of virtual machines and virtual networks in an academic environment and also specifically discusses sample projects on network security, which may not be feasible enough to be conducted in a physical network of personal computers; but could be conducted only using virtual machines.