Resource Requests Research Articles

Stability analysis on resource matching in crowdsourced manufacturing

Abstract Recently, the crowdsourced manufacturing concept attracts attention. In the crowdsourced manufacturing, each participant shares their manufacturing resources to improve asset efficiency. To realize this concept, it is important how to make matching between resource requests and offers which achieves high efficiency in fair way. In the view of fairness, matching stability is important. In this paper, we propose two stability indices: ‘number of blocking pairs’ and ‘loss caused by blocking pairs’. We evaluate the productivity efficiency and the matching stability of some representative optimization methods. The computational results show importance of resource matching selection considering features of crowdsourced manufacturing system.

Stability analysis on resource matching in crowdsourced manufacturing

Recently, the crowdsourced manufacturing concept attracts attention. In the crowdsourced manufacturing, each participant shares their manufacturing resources to improve asset efficiency. To realize this concept, it is important how to make matching between resource requests and offers which achieves high efficiency in fair way. In the view of fairness, matching stability is important. In this paper, we propose two stability indices: ‘number of blocking pairs’ and ‘loss caused by blocking pairs’. We evaluate the productivity efficiency and the matching stability of some representative optimization methods. The computational results show importance of resource matching selection considering features of crowdsourced manufacturing system.

Integration of a heterogeneous compute resource in the ATLAS workflow

With the ever-growing amount of data collected with the experiments at the Large Hadron Collider (LHC), the need for computing resources that can handle the analysis of this data is also rapidly increasing. This increase will even be amplified after upgrading to the High Luminosity LHC [1]. High-Performance Computing (HPC) and other cluster computing resources provided by universities can be useful supplements to the resources dedicated to the experiment as part of the Worldwide LHC Computing Grid (WLCG) for data analysis and production of simulated event samples. Freiburg is operating a combined Tier2/Tier3, the ATLAS-BFG [2]. The shared HPC cluster "NEMO" at the University of Freiburg has been made available to local ATLAS [3] users through the provisioning of virtual machines incorporating the ATLAS software environment analogously to the bare metal system of the local ATLAS Tier2/Tier3 centre. In addition to the provisioning of the virtual environment, the on-demand integration of these resources into the Tier3 scheduler in a dynamic way is described. In order to provide the external NEMO resources to the user in a transparent way, an intermediate layer connecting the two batch systems is put into place. This resource scheduler monitors requirements on the user-facing system and requests resources on the backend-system.

Cluster based Hybrid Approach to Task Scheduling in Cloud Environment

Cloud computing technology enables sharing of computer system resources among users through internet. Many numbers of users may request for sharable resources from a cloud. The sharable resources must be effectively distributed among requested users with in a less amount of time. Task scheduling is one of the ways of handling the user requests effectively in a cloud environment. There were many existing biologically inspired optimization techniques worked with task scheduling problems. The proposed paper is aimed at clubbing clustering techniques with biologically inspired optimization algorithms for deriving better results. A new hybrid methodology KPSOW (K-means with PSO using weights) has been proposed in the paper, which makes use of the strengths of both the K-means and PSO algorithms with the inclusion of weights concept. The results have shown that KPSOW has made considerable changes in reducing the makespan and improves the utilization of computing resources in the cloud.

Deployment of Fog Computing During Hajj Season: A Proposed Framework

With the considerable growth of mobile computing and IoT devices these days, cloud computing faces some difficulties in responding to a sudden, momentous increase in high number of requests for network resources. In 2012, Cisco introduced the idea of bringing the computation process to the periphery of the network and geographically closer to the end users to offload the main cloud and bandwidth, leading to a faster response time and less cost. Leveraging on this concept, the cloud computing and IoT technologies, we propose in this paper a novel fog-computing related framework to mitigate the problem of bandwidth bottleneck and packet delay during the Hajj season in Saudi Arabia, where millions of pilgrims are gathered in a small area for a short period of time along with all service providers, health and security personnel. We also demonstrate the potential applicability of our proposed framework in terms of efficient data entry and information retrieval.

Improving the Scheduling Efficiency of a Global Multi-Core HTCondor Pool in CMS

Scheduling multi-core workflows in a global HTCondor pool is a multi-dimensional problem whose solution depends on the requirements of the job payloads, the characteristics of available resources, and the boundary conditions such as fair share and prioritization imposed on the job matching to resources. Within the context of a dedicated task force, CMS has increased significantly the scheduling efficiency of workflows in reusable multi-core pilots by various improvements to the limitations of the GlideinWMS pilots, accuracy of resource requests, efficiency and speed of the HTCondor infrastructure, and job matching algorithms.

Server Consolidation Energy-Saving Algorithm Based on Resource Reservation and Resource Allocation Strategy

Energy consumption has overtaken equipment costs given the growth of computing power, thereby becoming the dominant cost to data centers. Constrained by ensuring that peak resource requirements of VMs are met, the effective use of resource fragments is an effective means of energy conservation. We apply resource reservation to the resource-utilization-aware energy efficient server consolidation algorithm (RUAEE), and propose a server consolidation energy-saving algorithm (SCES). We adjust the allocations of VMs by computing the resource reservation and resource allocation ratio dynamically. This technique maintains the host resource utilization to within a reasonable range, reduces resource fragmentation and reserves resources to satisfy the peak resource requests simultaneously. Compared with the RUAEE algorithm, our algorithm can reduce the risk of system overload and improve the stability of the system. The experimental results based on the actual workload of the Google cluster show that the algorithm is effective at reducing resource waste, improving system stability, and achieving energy savings.

Autonomous Resource Request Transaction Framework Based on Blockchain in Social Network

Autonomous Resource Request Transaction Framework Based on Blockchain in Social Network

Fair Share Scheduler for OpenNebula

A small Cloud infrastructure for scientific computing likely operates in a saturated regime, which imposes to optimize the allocation of resources. Tenants typically pay a priori for a fraction of the overall resources. Within this business model, an advanced scheduling strategy is needed in order to optimize the data centre occupancy. FaSS, a Fair Share Scheduler service for OpenNebula, addresses this issue by satisfying resource requests according to an algorithm, which prioritizes tasks according to an initial weight and to the historical resource usage of the project. In this proceedings, we are going to describe the implementation of FaSS Version 1.0, released in March 2017 as a product of the INDIGO-DataCloud project. We are also going to discuss the results of FaSS functional and stress tests performed at the Cloud infrastructure of the INFN-Torino computing centre.

Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the computing resources across edge servers. With the consideration of this issue, a Balanced Resource Scheduling Method, named BRSM, for trade‐offs between virtual machine (VM) migration cost and energy consumption of VM migrations for edge server management, named BRSM, is designed in this paper. Technically, we analyze the load conditions of edge servers and formulate the energy consumption of VM migrations and VM migration cost as a multi‐objective optimization problem. Then, we propose a dynamic resource scheduling method for WMAN to deal with the multi‐objective optimization problem. In addition, nondominated sorting genetic algorithm III (NSGA‐III) is adopted to generate optimal resource scheduling strategies. Finally, we conduct experiment simulations to testify the efficiency of the proposed method BRSM.

A novel QoS negotiation model based on intelligent learning technique in clouds

As more and more high-end applications have been deployed on cloud platforms, the quality-of-service (QoS) of cloud platform has become an important issue that need to be addressed. To achieve desirable QoS, an efficient negotiation model plays a critical role because cloud application often requires plenty of resource during their execution. In this paper, we propose a novel QoS negotiation model which applies intelligent learning technique to adjust the negotiation policy, in this way, QoS agreement between application and resource provider can be quickly achieved even when multiple resource requests are involved concurrently. The proposed negotiation model is implemented in an integrated QoS negotiation framework, in which negotiation agents help to make resource reservations while allocations agents are responsible for committing those reservation requests. The proposed framework is tested in a real cloud platform, and the results indicate that it can significantly improve the negotiation efficiency comparing with other negotiation methods. In addition, the experimental results also show that by using the proposed negotiation model, large-scale workflow applications can reduce their makespan by about 7%~11%.

Online cost-rejection rate scheduling for resource requests in hybrid clouds

Abstract The hybrid cloud computing model has attracted considerable attention in recent years. Due to security and controllability of private cloud, some resource requests are required to be scheduled in private parts of hybrid cloud. However, these requests could be rejected because of the resource limitation of private parts of hybrid cloud. In this paper, all resource requests are classified into two categories, i.e., the special requests required to process in private cloud, and the normal requests insensitive in private or public clouds. By considering the normal requests undivided to dispatch a cloud and divided to dispatch different clouds, we propose the online cost-rejection rate scheduling strategy (OCS) for the normal requests undivided, and the online scheduling strategy for some normal requests divided (OCS-divided), which could make suitable requests placement decisions in real-time and minimize the cost of renting public cloud resources with a low rate of rejected requests. Then, we transform both online models into two one-shot optimization problems by taking advantage of Lyapunov optimization techniques, and employ the optimal decay algorithm to solve the one-shot problems. The simulation results demonstrate that our scheduling strategies can achieve the trade-off between cost and rejection rate, and process the real-time resource requests in hybrid clouds. OCS-divided can achieve an average cost saving of about 25% compared with OCS and maximize the resource utilization.

Integrated Pediatric Health Care in Pennsylvania: A Survey of Primary Care and Behavioral Health Providers.

This study reports on a statewide survey of medical and behavioral health professionals to advance the knowledge base on the benefits and obstacles to delivering integrated pediatric health care. Surveys distributed in 3 statewide provider networks were completed by 110 behavioral health specialists (BHSs) and 111 primary care physicians (PCPs). Survey content documented their perceptions about key services, benefits, barriers, and needed opportunities related to integrated care. Factor analyses identified 8 factors, and other items were examined individually. We compared responses by specialty group (BHS vs PCP) and integrated care experience (no vs yes). The findings revealed differences across domains by specialty subgroup. In several cases, BHS (vs PCP) respondents, especially those with integrated care experience, reported lower benefits, higher barriers, and fewer resource requests. The implications of these results for enhancing care integration development, delivery, training, and research are discussed along with the study's limitations and empirical literature.

Analyzing Random Access Collisions in Massive IoT Networks

The cellular-based infrastructure is regarded as one of the potential solutions for massive Internet of Things (mIoT), where the random access (RA) procedure is used for requesting channel resources in the uplink data transmission. Due to the nature of the mIoT network with the sporadic uplink transmissions of a large amount of IoT devices, massive concurrent channel resource requests lead to a high probability of RA failure. To relieve the congestion during the RA in mIoT networks, we model RA procedure and analyze as well as evaluate the performance improvement due to different RA schemes, including power ramping (PR), back-off (BO), access class barring (ACB), hybrid ACB and back-off schemes, and hybrid power ramping and back-off (PR&BO). To do so, we develop a traffic-aware spatio-temporal model for the contention-based RA analysis in the mIoT network, where the signal-to-noise-plus-interference ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared to existing literature that only models collision from the single-cell perspective, we model both SINR outage and the collision from the network perspective. Based on this analytical model, we derive the analytical expression for the RA success probabilities to show the effectiveness of different RA schemes. We also derive the average queue lengths and the average waiting delays of each RA scheme to evaluate the packets accumulation status and packets serving efficiency. Our results show that our proposed PR&BO scheme outperforms other schemes in heavy traffic scenarios in terms of the RA success probability, the average queue length, and the average waiting delay.

Resource Request Based Energy Efficient Heuristic for Server Offloading in Cloud Computing Environment

Increasing demand for high computing power led to the establishment of large-scale data centers. A data center is a collection of millions of servers. These large-scale data centers consume a huge amount of electrical energy. Managing these servers for provisioning and de-provisioning of resources in an automatic and efficient way is a great challenge. We attempt to minimise the power consumption by reducing the number of servers and maximizing the resource utilization of the servers that are in use through virtualization of resources. We work upon Virtual Machine Consolidation for consolidating them on fewer servers for maximizing resource utilization. In this article, we propose a resource request based heuristic for offloading the overloaded servers to optimise power consumption efficiency.

Toward maximization of profit and quality of cloud federation: solution to cloud federation formation problem

The emergence of cloud computing has led to an astronomical growth in the computing services provided by vendors over the cloud interface. This has led to the paradigm of cloud federations where a group of CSPs collaborate to form a federation for seamless provisioning of resource requests. In this paper, cloud federation formation framework is modeled as a multi-objective optimization problem with the trade-off between profit and QoS. Federation formation algorithms try to maximize the federation profit while maintaining a balance between the QoS and the profit of the members of the federation. We have applied Linear Scalarization as well as $$\varepsilon $$ -constraint method to find the pareto-optimal solution to this multi-objective optimization problem. A heuristic-based algorithm for cloud federation formation following the integer linear program is proposed. We perform extensive experiments to investigate the performance of our proposed mechanism and show that our proposed mechanism yields optimized solution to the general problem of profit/QoS trade-off.

Autonomic Mobile Virtual Network Operators for Future Generation Networks

The effort of telecommunications operators in 5G design and implementation is oriented to effective and efficient verticals support. Full network softwarization will deploy tools such as software-defined networks and network functions virtualization to allow dynamic service provisioning on the same physical infrastructure. However, current 5G proposed architecture still requires further enhancement to guarantee full real-time on-demand services. Current virtualization technologies still miss complete automation and adaptation, while they do not have dynamic negotiation capabilities. These issues are bounding the desired complete automation and the significant reduction in OPEX and CAPEX. Thus, this article proposes a full architecture to realize autonomic mobile virtual network operators. These autonomic virtual operators can be deployed by Internet service providers to guarantee efficient and effective network adaptation to unexpected events and real-time resource requests. The objective of an AMVNO is to exploit softwarization of networks and experiential intelligence to play the role of a local operator by performing network management and service release without human intervention. Its autonomic character can also provide proactive actions against unwanted network states.

Migration cost and profit oriented cloud federation formation: hedonic coalition game based approach

Cloud federation has paved the way for cloud service providers (CSP) to collaborate with other CSPs to serve users’ resource requests, which are prohibitively high for any single CSP during peak time. Moreover, to entice different CSPs to participate in federation, it is necessary to maximize the profit of all CSPs involved in the federation. Further, federation enables overloaded CSPs to distribute their load among other underloaded member CSPs of federation by migrating the virtual machines (VM). Migration of VM among member CSPs of federation, also enables to increase the reliability and availability of cloud services on occurrence of faults in the datacenters of CSPs. Thus it becomes important for CSPs to form a federation with other CSPs, in such a way that the migration cost of VMs between CSPs of the same federation is minimized and simultaneously profit of CSPs in federation is maximized. In this paper, we model the problem of forming federation among CSPs as a hedonic coalition game, with a utility function depending on profit and migration cost, with the objective of maximizing the former and minimizing the latter. We propose an algorithm to solve this hedonic game and compare its performance with other existing game-theory based cloud federation formation mechanisms.

A hierarchical structure for optimal resource allocation in geographically distributed clouds

Due to the growth of users’ requests for various resources in cloud computing, the optimal resource allocation is one of the most important challenges in cloud environments. The optimal resource allocation is achieved by considering user requirements stated in Service Level Agreements (SLAs) and the Quality of Services (QoSs) provided by resources. Since some user requirements (objectives) conflict with some others, a optimal trade-off between them is required in the selection of resources. Obtaining such a trade-off is a complicated and NP-hard problem because we may come up with a lot of permutations (choices) of the resources with the desired QoS. If a cloud environment is geographically distributed, the problem becomes more complicated because in the geographically distributed cloud there are a lot of candidate datacenters with qualified resources. The user requirements considered in this paper are availability and reliability of resources should be maximized and resource cost and response time should be minimized as well as the minimization of the network traffic. The maximization of and the minimization of the requirements conflict with each other; therefore a trade-off between them is required. In this paper, a hierarchical structure with two resource selections methods called Simplex Linear Programming (SLP) Method and GrEA-based method are used where the hierarchical structure is used to present connections between distributed datacenters and the methods are used to select optimal resources among the datacenters. The most important feature of the hierarchical structure is to prevent the occurrence of an accumulation of requests in a datacenter leading to increase the rate of finding optimal VMs. Moreover, in most studies on geographically distributed clouds, one user requirement is considered for the optimal selection of resources; however, in this study, datacenters are selected based on 4 user requirements as well as the network traffic. Moreover, while in most studies, the requests for one user is considered at one time, this work does the requests for several users sent to the cloud simultaneously. Our results showed an optimal trade-off between user conflicting requirements and the resource utilization by 92%. Moreover, the performance indexes coverage ratio and the maximum spread indexes showed more performance compared to related studies.

A Survey of Resource Management Challenges in Multi-cloud Environment: Taxonomy and Empirical Analysis

Cloud computing has seen a great deal of interest by researchers and industrial firms since its first coined. Different perspectives and research problems, such as energy efficiency, security and threats, to name but a few, have been dealt with and addressed from cloud computing perspective. However, cloud computing environment still encounters a major challenge of how to allocate and manage computational resources efficiently. Furthermore, due to the different architectures and cloud computing networks and models used (i.e., federated clouds, VM migrations, cloud brokerage), the complexity of resource management in the cloud has been increased dramatically. Cloud providers and service consumers have the cloud brokers working as the intermediaries between them, and the confusion among the cloud computing parties (consumers, brokers, data centres and service providers) on who is responsible for managing the request of cloud resources is a key issue. In a traditional scenario, upon renting the various cloud resources from the providers, the cloud brokers engage in subletting and managing these resources to the service consumers. However, providers’ usually deal with many brokers, and vice versa, and any dispute of any kind between the providers and the brokers will lead to service unavailability, in which the consumer is the only victim. Therefore, managing cloud resources and services still needs a lot of attention and effort. This paper expresses the survey on the systems of the cloud brokerage resource management issues in multi-cloud environments.