A Framework for QoS-aware Execution of Workflows over the Cloud

The Cloud Computing paradigm is providing system architects with a new powerful tool for building scalable applications. Clouds allow allocation of resources on a "pay-as-you-go" model, so that additional resources can be requested during peak loads and released after that. However, this flexibility asks for appropriate dynamic reconfiguration strategies. In this paper we describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for executing workflows involving Web Services hosted in a Cloud environment. SAVER allows execution of arbitrary workflows subject to response time constraints. SAVER uses a passive monitor to identify workload fluctuations based on the observed system response time. The information collected by the monitor is used by a planner component to identify the minimum number of instances of each Web Service which should be allocated in order to satisfy the response time constraint. SAVER uses a simple Queueing Network (QN) model to identify the optimal resource allocation. Specifically, the QN model is used to identify bottlenecks, and predict the system performance as Cloud resources are allocated or released. The parameters used to evaluate the model are those collected by the monitor, which means that SAVER does not require any particular knowledge of the Web Services and workflows being executed. Our approach has been validated through numerical simulations, whose results are reported in this paper

FACTORS INFLUENCING JOB REJECTIONS IN CLOUD ENVIRONMENT

The IT organizations invests heavy capital by consuming large scale infrastructure and advanced operating platforms. The advances in technology has resulted in emergence of cloud computing, which is promising technology to achieve the aforementioned objective. At the peak hours, the jobs arriving to the cloud system are normally high demanding efficient execution and dispatch. An observation that has been carried out in this paper by capturing a job arriving pattern from a monitoring system explains that most of the jobs get rejected because of lack of efficient technology. The job rejections can be controlled by certain factors such as job scheduling and load balancing. Therefore, in this paper the efficiency of Round Robin (RR) scheduling strategy used for job scheduling and Shortest Job First Scheduling (SJFS) technique used for load balancing in reducing the job rejections are analyzed. Further, a proposal for an effective load balancing approach to avoid deadlocks has been discussed

Hard-deadline Constrained Workflows Scheduling Using Metaheuristic Algorithms

AbstractAn efficient scheduling is the essential part of complex scientific applications processing in computational distributed environments. The computational complexity comes as from environment heterogeneity as from the application structure that usually is represented as a workflow which contains different linked tasks. A lot of well-known techniques were proposed by different scientific groups. The most popular of them are based on greedy list-based heuristics or evolutionary metaheuristics. In this paper we investigate the applicability of previously developed metaheuristic algorithm – coevolutional genetic algorithm (CGA) for scheduling series of workflows with hard deadlines constraints

Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM: Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM

This paper presents an avoid deadlock resource allocation (ADRA) for model V VM-out-of-N PM since cloud computing is a new computing paradigm composed of grid computing, distributed computing and utility concepts. Cloud computing presents a different resource allocation paradigm than either grids or distributed systems. Cloud service providers dynamically scale virtualized computing resources as a service over the internet. Due to variable number of users and limited resources, cloud is prone to deadlock at very large scale. Resource allocation and the associated deadlock avoidance is problem originated in the design and the implementation of the distributed computing, grid computing. In this paper, a new concept of free space cloud is proposed to avoid deadlock by collecting available free resource from all allocated users. New algorithms are developed for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform.  An experiment is tested in CloudSim. The performance of resource pool manager is evaluated by using CloudSim and resource utilization and indicating good results

Optimum Resource Allocation using Specification Matching and Priority Based Method in Cloud

Cloud computing is summed up as a different model for allowing favorable, network as per demand to use shared devices of computational resources which are collected and then released with marginal management effort or interaction with any client or any service provider. Cloud computing is a well-known technology in the pasture of information technology that provides computing as a service. In cloud computing environment the resources are provisioned on the basis of demand, as and when required. A large number of cloud users can request a number of cloud services at the same time. Due to increase in the usage of cloud computing there is a need for a efficient and effective resource allocation algorithm which can be used for proper usage of the resources and also check that the resource is not wastage. In this we propose a priority based resource allocation algorithm which can be used for proper allocation of resources and also the resources are allocated efficiently and effectively. In this paper, two strategies are proposed for the purpose of optimum resource allocation in which the first approach uses the concept of specification matching and second uses the concept of priority based approach. In the first approach, different types of resources (virtual machine) are allocated by taking three parameters into consideration: processing element, main memory, and network bandwidth. In the second approach, one parameter is considered namely: Priority. In both strategies, users are allowed to submit the parameters during cloudlet submission. The user inserted parameters will then be considered while allocating resources to them. The objectives of this research are to improve utilization of resources and reduce the request loss

A distributed approach to dynamic vm management

Abstract-Computing today is increasingly moving into largescale virtualized data centres, offering computing resources in the form of virtual machines (VMs) on a pay-per-usage basis. In order to minimize costs, VMs should be consolidated on as few physical machines (PMs) as possible, switching idle PMs into a power saving mode. It may be necessary to dynamically allocate and reallocate VMs to PMs in order to meet highly dynamic VM resource requirements. The problem of assigning VMs to PMs is known to be NP-Hard. Most solutions focus on a centralized approach, with a single management node making allocation decisions periodically. This approach suffers from poor scalability and the existence of a single point of failure. We present a fully distributed approach to dynamic VM management, and evaluate our approach using a simulation tool. Results indicate that the distributed approach can achieve similar performance to the centralized solution, while eliminating the single point of failure and reducing the network bandwidth required for management

Entangled N-photon states for fair and optimal social decision making

Situations involving competition for resources among entities can be modeled by the competitive multi-armed bandit (CMAB) problem, which relates to social issues such as maximizing the total outcome and achieving the fairest resource repartition among individuals. In these respects, the intrinsic randomness and global properties of quantum states provide ideal tools for obtaining optimal solutions to this problem. Based on the previous study of the CMAB problem in the two-arm, two-player case, this paper presents the theoretical principles necessary to find polarization-entangled N-photon states that can optimize the total resource output while ensuring equality among players. These principles were applied to two-, three-, four-, and five-player cases by using numerical simulations to reproduce realistic configurations and find the best strategies to overcome potential misalignment between the polarization measurement systems of the players. Although a general formula for the N-player case is not presented here, general derivation rules and a verification algorithm are proposed. This report demonstrates the potential usability of quantum states in collective decision making with limited, probabilistic resources, which could serve as a first step toward quantum-based resource allocation systems.Comment: 22 pages and 7 figures, version 1.1 of July 27th 202