Virtual Machine Capacity Research Articles

Mean makespan task scheduling approach for the edge computing environment

Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics.

Tweaked optimization based quality aware VM selection method for effectual placement strategy

Cloud computing has become a standard and promising distributed computing framework for the provision of on-demand computing resources and pay-per-use concepts. Operations of these computing resources result in maximum power consumption, enraged cost and high Co2 emission to the environment. The major difficulties faced when accessing cloud data center are SLA violations, increased time, less utilization of resources, high consumption of power and energy. Hence, considering these difficulties, a novel virtual machine (VM) selection approach is proposed to minimize the constraints while maintaining the SLA. First, based on the assumptions of VMs and physical machines (PMs), the overutilized hosts are detected using a static threshold approach, while underutilized hosts are identified based on the utilized resources. After load detection, the VMs that need to be migrated over other PMs are selected using the tweaked chimp optimization algorithm (TCOA). After selecting VMs without influencing the capacity of other VMs, the placement process is performed over other PMs using a power aware best fit decreasing approach. The proposed approach can greatly improve the QoS by selecting the optimal VMs that need to be migrated. Cloudsim is used as a simulation tool, and the results are compared with existing techniques in terms of migration time, energy consumption, SLA violation per host and so on to prove the superiority. The energy consumption of the proposed model is obtained to be 195.3 kWh, the overall SLA violation rate is attained to be 0.032%, and the migration time for 500 virtual machines is 8.72 s

HYBRID LOAD BALANCING POLICY TO OPTIMIZE RESOURCE DISTRIBUTION AND RESPONSE TIME IN CLOUD ENVIRONMENT

Load balancing and task scheduling are the main challenges in Cloud Computing. Existing load balancing algorithms have a drawback in considering the capacity of virtual machines while distributing loads among them. The proposed algorithm works toward solving existing issues, such as fair load distribution, avoiding underloading and overloading, and improving response time. It implements best practices of Throttled load balancing algorithm and Equally Shared Current Execution algorithm. Virtual machines are selected based on the ratio of their bandwidth and load allocation count. Requests are sent to a Virtual Machine with higher bandwidth and lower load allocation count. Proposed algorithm checks for the availability of VM based on their capacity. This process is performed by selecting two VMs and comparing their vmWeight capacity. The one with the least vmWeight is selected. CloudAnalyst is used for simulation, response time evaluation, and resource utilization evaluation. The simulation result of the proposed algorithm is compared with three well-known load-balancing algorithms. These are Round Robin, Throttled Load balancing algorithm, and Enhanced Active Monitoring. Load-balancing Proposed Algorithm selects VMs based on their Algorithm. The proposed algorithm has improved over other algorithms in load distribution, response time, and resource utilization. All virtual machines in the data centers are loaded with a relatively equal number of tasks according to their capacity. This resulted in fair resource sharing and load distribution.

BLMDP: A new bi-level Markov decision process approach to joint bidding andtask-scheduling in cloud spot market

In the cloud computing market (CCM), computing services are traded between cloud providers and consumers in the form of the computing capacity of virtual machines (VMs). The Amazon spot market is one of the most well-known markets in which the surplus capacity of data centers is auctioned off in the form of VMs at relatively low prices. For each submitted task, the user can offer a price that is higher than the current price. However, uncertainty in the market environment confronts the user with challenges such as the variable price of VMs and the variable number of users. An appropriate strategy should both maximize the user?s utility and determine the best bid for him/her. In this paper, we aim to solve the problem of joint minimizing the cost of processing tasks and maximizing user satisfaction. Our proposed method, which we call bi-level Markov decision-making process (BLMDP), works on two levels. At the top level, it selects the most appropriate user bids and adjusts the spot price to minimize the cost of VMs on the cloud provider side. At a low level, it decides to admit tasks in such a way as to maximize the user side satisfaction. Performance evaluation based on real data collected from Amazon web services shows that BLMDP manages to minimize cloud provider's costs and maximize user gain more effectively compared to heuristic methods.

An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

Cloud Computing provides various services to the customer in a flexible and reliable manner. Virtual Machines (VM) are created from physical resources of the data center for handling huge number of requests as a task. These tasks are executed in the VM at the data center which needs excess hosts for satisfying the customer request. The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time. This process is carried out based on various algorithms which follow a predefined capacity of source VM leads to the capacity issue at the destination VM. The proposed VM migration technique performs the migration process based on the request of the requesting host machine. This technique can perform in three ways namely single VM migration, Multiple VM migration and Cluster VM migration. Common Deployment Manager (CDM) is used to support through negotiation that happens across the source host and destination host for providing the high quality service to their customer. The VM migration requests are handled with an exposure of the source host capabilities. The proposed analysis also uses the retired instructions with execution by the hypervisor to achieve high reliability. The objective of the proposed technique is to perform a VM migration process based on the prior knowledge of the resource availability in the target VM.

High Efficient Virtual Machine Migration Using Glow Worm Swarm Optimization Method for Cloud Computing

In this paper, high efficient Virtual Machine (VM) migration using GSO algorithm for cloud computing is proposed. This algorithm contains 3 phases: (i) VM selection, (ii) optimum number of VMs selection, (iii) VM placement. In VM selection phase, VMs to be migrated are selected based on their resource utilization and fault probability. In phase-2, optimum number of VMs to be migrated are determined based on the total power consumption. In VM placement phase, Glowworm Swarm Optimization (GSO) is used for finding the target VMs to place the migrated VMs. The fitness function is derived in terms of distance between the main server and the other server, VM capacity and memory size. Then the VMs with best fitness functions are selected as target VMs for placing the migrated VMs. The proposed algorithms are implemented in Cloudsim and performance results show that PEVM-GSO algorithm attains reduced power consumption and response delay with improved CPU utilization.

An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

Purpose The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms. Design/methodology/approach This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions. Findings The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique. Originality/value This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

Improving MapReduce scheduler for heterogeneous workloads in a heterogeneous environment

SummaryBig data is largely influencing business entities and research sectors to be more data‐driven. Hadoop MapReduce is one of the cost‐effective ways to process large scale datasets and offered as a service over the Internet. Even though cloud service providers promise an infinite amount of resources available on‐demand, it is inevitable that some of the hired virtual resources for MapReduce are left unutilized and makespan is limited due to various heterogeneities that exist while offering MapReduce as a service. As MapReduce v2 allows users to define the size of containers for the map and reduce tasks, jobs in a batch become heterogeneous and behave differently. Also, the different capacity of virtual machines in the MapReduce virtual cluster accommodate a varying number of map/reduce tasks. These factors highly affect resource utilization in the virtual cluster and the makespan for a batch of MapReduce jobs. Default MapReduce job schedulers do not consider these heterogeneities that exist in a cloud environment. Moreover, virtual machines in MapReduce virtual cluster process an equal number of blocks regardless of their capacity, which affects the makespan. Therefore, we devised a heuristic‐based MapReduce job scheduler that exploits virtual machine and MapReduce workload level heterogeneities to improve resource utilization and makespan. We proposed two methods to achieve this: (i) roulette wheel scheme based data block placement in heterogeneous virtual machines, and (ii) a constrained 2‐dimensional bin packing to place heterogeneous map/reduce tasks. We compared heuristic‐based MapReduce job scheduler against the classical fair scheduler in MapReduce v2. Experimental results showed that our proposed scheduler improved makespan and resource utilization by 45.6% and 47.9% over classical fair scheduler.

Load prediction analysis based on virtual machine execution time using optimal sequencing algorithm in cloud federated environment

Virtual machine (VM) prediction and an effective resource management are the attractive areas in the cloud environment. VM prediction is an important task to execute the jobs for delay minimization and unnecessary states avoidance. Cloud computing attracted towards the increase in a number of applications that run on remote servers in parallel manner. Increase in parallelism reduces the CPU utilization adversely. Hence, the proper VM prediction and management are necessary stages in provisioning scheme. Also time required for allocating jobs is more in existing algorithms due to the number of computations involved. Therefore a novel algorithm is required to improve the performance of the job allocation with makespan reduction. In this paper the new algorithm is proposed that includes the VM capacity and execution time for load prediction and performance improvement purpose. Our proposed research work utilizes the VM clustering and optimization algorithms to improve job sequencing performance. The cost computation prior to clustering includes the VM capacity as a major factor. Clustering of VM with high-cost and isolation of low-cost and high-cost clusters reduces the searching time of VM and solve the imbalance state problem in traditional methods. The optimization algorithm with suitable initialization function reduces the time and steps for selection of VM for suitable job. The proposed model outperformance is established by the selected parameters.

Performance analysis of the reserve capacity policy for dynamic VM allocation in a SaaS environment

We consider a periodic-review provision scheme with constant inspection intervals for allocating dynamically virtual machines (VMs) in a Software-as-a Service (SaaS) environment. At each interval, we determine how many virtual machines (VMs) to provisioned or de-provision using a simple heuristic referred to as the reserve capacity policy, since it maintains a fixed reserve capacity of VMs. We analyze the performance of the reserve capacity policy within the context of a periodic-review provision scheme using a Markov Chain embedded at the inspection intervals. We assume a single stream of jobs with each job requiring a single VM. Jobs arrive in a Poisson fashion and the execution time of a job in a VM is exponentially distributed. We calculate the probability distribution of the number of customers in the system, the number of in-service VMs, the utilization, and the queue-length distribution of the waiting customers. The embedded Markov Chain is solved numerically. For cases where the underlying transition matrix is very large, we have proposed approximations and showed that they have a root mean square error (RMSE) of less than 2%.

A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT

Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, a multi-objective load balancing algorithm has been proposed to avoid deadlocks and to provide proper utilization of all the virtual machines (VMs) while processing the requests received from the users by VM classification. The capacity of virtual machine is computed based on multiple parameters like MIPS, RAM and bandwidth. Heterogeneous virtual machines of different MIPS and processing power in multiple data centers with different hosts have been created in cloud simulator. The VM’s are divided into 2 clusters using K-Means clustering mechanism in terms of processor MIPS, memory and bandwidth. The cloudlets are divided into two categories like High QOS and Low QOS based on the instruction size. The cloudlet whose task size is greater than the threshold value will enter into High QOS and cloudlet whose task size is lesser than the threshold value will enter into Low QOS. Submit the job of the user to the datacenter broker. The job of the user is submitted to the broker and it will first find the suitable VM according to the requirements of the cloudlet and will match VM depending upon its availability. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. This modified algorithm has an edge over the original approach in which each cloudlet build their own individual result set and it is later on built into a complete solution.

AN ENHANCED TASK ALLOCATION STRATEGY IN CLOUD ENVIRONMENT

Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Interne technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

Dynamic VM allocation in a SaaS environment

Given the costs associated with a cloud infrastructure, dynamic scheduling of virtual machines (VMs) can significantly lower costs while providing an acceptable service level. We develop a series of forecasting models for predicting demand for VMs in a cloud-based software used as a software-as-a-service (SaaS). These models are then used in a periodic-review provision model which determines how many VMs should be provisioned or de-provision at each inspection interval. A simple provisioning heuristic model is also proposed, whereby a fixed reserve capacity of VMs is continuously maintained. We evaluate and compare the performance of these models for different model parameters using historical data from the Virtual Computing Laboratory (VCL) at North Carolina State University.

A NOVEL APPROACH OF OPTIMIZING PERFORMANCE USING K-MEANS CLUSTERING IN CLOUD COMPUTING

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.Â

Deadline based scheduling for data-intensive applications in clouds

Cloud computing emerges as a new computing pattern that can provide elastic services for any users around the world. It provides good chances to solve large scale scientific problems with fewer efforts. Application deployment remains an important issue in clouds. Appropriate scheduling mechanisms can shorten the total completion time of an application and therefore improve the quality of service (QoS) for cloud users. Unlike current scheduling algorithms which mostly focus on single task allocation, we propose a deadline based scheduling approach for data-intensive applications in clouds. It does not simply consider the total completion time of an application as the sum of all its subtasks' completion time. Not only the computation capacity of virtual machine (VM) is considered, but also the communication delay and data access latencies are taken into account. Simulations show that our proposed approach has a decided advantage over the two other algorithms.

ASTAW: Auto-Scaling Threshold-based Approach for Web Application in Cloud Computing Environment

In recent years, the number of users and service providers are increasing in using cloud services so the accessibility and the effective management of the required resources, irrespective of the time and place, seem to be of great importance for both sides. Improving the performance and utilization of the cloud systems are gained by the auto-scaling of the applications; this is because of the fact that, some approaches have been proposed for auto scaling. This paper seeks to checking some value, based on the learning automata, for the scalability of the web applications, which combines virtual machine clusters and the learning automata in order to provide the best possible way for the scaling up and scaling down of the virtual machines. The results of this study indicate how an increased capacity of virtual machine which have been done by the value of thresholds could effect on SLA and overhead of responding. Cloud computing technology is currently one of the popular and developing technologies and a successful example of distributed computing. Cloud computing is a model for an easy provision of network access, based on demand, for a shared storage of configurable computing resources (i.e. networks, servers, applications, services, etc.), which is capable of being provided and released very quickly with minimal management efforts, and minimal interaction with the service provider (1,2). Cloud computing technology is an attempt to propose a new mechanism for the provision of the necessary infrastructures for the users and for the creation of the illusion of access to unlimited resources in the minds of the users. Those who work in the field of cloud computing technology have considered various advantages for it including flexibility, reliability, scalability, security, a decrease in costs, an unlimited capacity of the resources, etc. However, among all these capabilities, scalability seems to enjoy more popularity compared to the others and, one can find very few documents about cloud computing in which the issue of scalability has not been discussed. Since all applications, and in particular web applications, do not follow regular workload patterns, the scaling operations (i.e. scale up or scale down) must be carried out in real time and with minimal human intervention so that the recourses would be provided for the applications as soon as possible. Such a scaling of the resource, which is done automatically and with minimal human intervention, is called Auto-scaling (3). This paper seeks to propose a novel approach for the auto-scaling of the resources of the web applications. The proposed approach is derived from the threshold scalable algorithm and is based on the learning automata. The learning automata is defined using

AppRAISE: application-level performance management in virtualized server environments

Managing application-level performance for multitier applications in virtualized server environments is challenging because the applications are distributed across multiple virtual machines, and workloads are dynamic in their intensity and transaction mix resulting in time-varying resource demands. In this paper, we present AppRAISE, a system that manages performance of multi-tier applications by dynamically resizing the virtual machines hosting the applications. We extend a traditional queuing model to represent application performance in virtualized server environments, where virtual machine capacity is dynamically tuned. Using this performance model, AppRAISE predicts the performance of the applications due to workload changes, and proactively resizes the virtual machines hosting the applications to meet performance thresholds. By integrating feedforward prediction and feedback reactive control, AppRAISE provides a robust and efficient performance management solution. We tested AppRAISE using Xen virtual machines and the RUBiS benchmark application. Our empirical results show that AppRAISE can effectively allocate CPU resources to application components of multiple applications to meet end-to-end mean response time targets in the presence of variable workloads, while maintaining reasonable trade-offs between application performance, resource efficiency, and transient behavior.