Cloud scheduling with setup cost

Symbiotic Organism Search optimization based task scheduling in cloud computing environment

Hadoop MapReduce as a service from cloud is widely used by various research, and commercial communities. Hadoop MapReduce is typically offered as a service hosted on virtualized environment in Cloud Data-Center. Cluster of virtual machines for MapReduce is placed across racks in Cloud Data-Center to achieve fault tolerance. But, it negatively introduces dynamic/heterogeneous performance for virtual machines due to hardware heterogeneity and co-located virtual machine's interference, which cause varying latency for same task. Alongside, curbing number of intermediate records and placing reduce tasks on right virtual node are also important to minimize MapReduce job latency further. In this paper, we introduce Multi-Level Per Node Combiner to minimize the number of intermediate records and Dynamic Ranking based MapReduce Job Scheduler to place reduce tasks on right virtual machine to minimize MapReduce job latency by exploiting dynamic performance of virtual machines. To experiment and evaluate, we launched 29 virtual machines hosted in eight different physical machines to run wordcount job on PUMA dataset. Our proposed methodology improves overall job latency up to 33% for wordcount job.

BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments

Many firms have to deal with the problems of scheduling and transportation allocation. The problems of assembly scheduling mainly focus on how to arrange orders in proper sequence on the assembly line with the purpose of minimizing the maximum completion time before they are flown to their destinations. Transportation allocation problems arise in how to assign processed orders to transport modes in order to minimize penalties such as earliness and tardiness. The two problems are usually separately discussed due to their complexity. This paper simultaneously deals with these two problems for firms with multiple identical parallel machines. We formulate this problem as a mixed integer programming model. The problem belongs to the class of NP-complete combinatorial optimization problems. This paper develops a hybrid genetic algorithm to obtain a compromised solution within a reasonable CPU time. We evaluate the performance of the presented heuristic with the well-known GAMS/CPLEX software. The presented approach is shown to perform well compared with well-known commercial software.

HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment

Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments

This paper focuses on an integrated workload scheduling and pre-fetching model in a multimedia mobile cloud computing environment to enhance the performance of response time and reduce the cost to process multimedia data. The response time and cost optimization problems are presented along with the computation resources such as virtual machines (VMs) allocation, workload conservation, queueing stability constraints, and to overcome the total response time and cost, a heuristic approach of workload scheduling method is proposed. The integrated workload scheduling at pre-fetcher and cloud are considered to study the effects of various parameters such as VM’s processing speed, pre-fetcher’s utilization, the user requests arrival rate. The performance analysis results reveal that the cost and transmission speed are directly relevant factors, meaning that, once the rate of data transmission is increasing, the cost is also increasing and vice versa. Hence, the time and cost efficient workload scheduling is essential to satisfy both delay and cost in pre-fetcher enabled multimedia cloud systems.

In the current scenario, Cloud computing carved itself as an emerging technology which enables the organization to utilize hardware, software and applications without any upfront cost over the internet. The challenge before the cloud service provider is, how efficiently and effectively the underlying computing resources like virtual machines, network, storage units, and bandwidth etc. should be managed so that no computing device is in under-utilization or over-utilization state in a dynamic environment. A good task scheduling technique is always required for the dynamic allocation of the task to avoid such a situation. Through this paper we are going to present the Genetic Algorithm based task scheduling technique, which will distribute the load effectively among the virtual machine so that the overall response time (QoS) should be minimal. A comparison of this Genetic Algorithm based task scheduling technique is performed on CloudSim simulator which shows that, this will outperform the existing techniques like Greedy based, First - Come first - Serve (FCFS) techniques.

The virtual machine (VM) scheduling problem in cloud brokers that support cloud bursting is fraught with uncertainty due to the on-demand nature of Infrastructure as a Service (IaaS) VMs. Until a VM request is received, the scheduler does not know in advance when it will arrive or what configurations it demands. Even when a VM request is received, the scheduler does not know when the VM's lifecycle expires. Existing studies begin to use deep reinforcement learning (DRL) to solve such scheduling problems. However, they do not address how to guarantee the QoS of user requests. In this paper, we investigate a cost optimization problem for online VM scheduling in cloud brokers for cloud bursting to minimize the cost spent on public clouds while satisfying specified QoS restrictions. We propose DeepBS, a DRL-based online VM scheduler in a cloud broker which learns from experience to adaptively improve scheduling strategies in environments with non-smooth and uncertain user requests. We evaluate the performance of DeepBS under two request arrival patterns which are respectively based on Google and Alibaba cluster traces, and the experiments show that DeepBS has a significant advantage over other benchmark algorithms in terms of cost optimization.

Cloud computing becoming one of the most advanced and promising technologies in these days for information technology era. It has also helped to reduce the cost of small and medium enterprises based on cloud provider services. Resource scheduling with load balancing is one of the primary and most important goals of the cloud computing scheduling process. Resource scheduling in cloud is a non-deterministic problem and is responsible for assigning tasks to virtual machines (VMs) by a servers or service providers in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. So in this paper, we presented a model deep learning based resource scheduling and load balancing using multidimensional queuing load optimization (MQLO) algorithm with the concept of for cloud environment Multidimensional Resource Scheduling and Queuing Network (MRSQN) is used to detect the overloaded server and migrate them to VMs. Here, ANN is used as deep learning concept as a classifier that helps to identify the overloaded or under loaded servers or VMs and balanced them based on their basis parameters such as CPU, memory and bandwidth. In particular, the proposed ANN-based MQLO algorithm has improved the response time as well success rate. The simulation results show that the proposed ANN-based MQLO algorithm has improved the response time compared to the existing algorithms in terms of Average Success Rate, Resource Scheduling Efficiency, Energy Consumption and Response Time.

In elastic cloud computing environment, multiple virtual machines may reside in the same physical machine for services consolidation. For the same residential guest domains or multi-tiered hosting services, the inter-domain communications are complex and frequent. However, traditional inter-domain communications are conducted through the virtual network interfaces of both sending and receiving virtual machines. Such TCP/IP networking communications involve lengthy communication path and frequent kernel interactions and thus downgrade the I/O performance of virtual machines. In this paper, we present an optimized inter-domain communication approach based on shared memory to improve the communication performance of multiple virtual machines residing in the same physical machine. In our approach, the sending data is shared in memory, not transferred via TCP/IP stack. The communication data are directly mapped into user space of the virtual machine and therefore it reduces useless system calls and context switches. The memory is shared via a customized shared device kernel module with bidirectional event channel resided in both communicating virtual machines. We use state flags in circular buffer to reduce wait and notify operations and thus reduce the system calls. Experiments show that our proposed approach can provide 5 time higher throughput and 2.5 times less latency compared to TCP/IP communication path.

Cloud computing has been one of the revolutionary breakthroughs of this decade in the ICT world and its popularity is soaring more than ever. More and more data centers are being deployed in order to accommodate the physical resources needed by cloud systems. As an important side effect the global energy demand of data centers are also on the rise. In the meantime the advancement in virtualization technologies has made migrating virtual machines from one host to another without shutting them down possible. Therefore the optimization of data center operations through the dynamic placement of virtual machines became a reality. This paper formalizes the well-studied cloud scheduling problem in a matching theoretical model in which the virtual machine to physical server mapping is translated into a stable matching problem. We build on an advanced algorithm from the matching theory domain in order to find the most accommodating scheduling arrangement. Hindered by the complexity of the algorithm, we evaluate various heuristics in numerical simulations of cloud environments. After the verification of the selected heuristic algorithm, we present the implementation of the proposed method as a custom compute scheduler for OpenStack.

Latency based group discovery algorithm for network aware cloud scheduling

Processes on virtual machines can migrate from a host server to a guest server by migrating the virtual machines. In this paper, we propose a DMMV (Dynamic Migration of Multiple Virtual machines) algorithm where virtual machines migrate from a host server to a more energy-efficient guest server. Here, virtual machines are dynamically suspended and resumed depending on the number of processes. In addition, one or more than one virtual machine is selected to migrate from a host server to a guest server. The number of virtual machines to migrate is decided so as to minimize the electric energy consumption of the host and guest servers. In the evaluation, we show not only the total electric energy consumption and active time of servers but also the average execution time of processes can be reduced in the DMMV algorithm compared with other algorithms.

The recent increase in availability of Infrastructure-as-a-Service (IaaS) computing clouds provides a new way for researchers to run complex scientific applications. However, using cloud resources for a large number of research jobs requires significant effort and expertise. Furthermore, running jobs on many different clouds presents even more difficulty. In order to make it easy for researchers to deploy scientific applications across many cloud resources, we have developed a virtual machine resource manager (Cloud Scheduler) for distributed compute clouds. In response to a user's job submission to a batch system, the Cloud Scheduler manages the distribution and deployment of user-customized virtual machines across multiple clouds. We describe the motivation for and implementation of a distributed cloud using the Cloud Scheduler that is spread across both commercial and dedicated private sites, and present some early results of scientific data analysis using the system.