Resource Co-allocation Research Articles

Micro-foundations of dynamic capabilities to facilitate university technology transfer.

Within the university-industry ecosystem, improvement and innovation of technology transfer involve implementing appropriate dynamic capabilities. To answer the question-What are the micro-foundations of dynamic capabilities in university technology transfer?-this study investigates in-depth organizational-level dynamic capabilities in transferring university-based knowledge to business and society. Two qualitative case studies were deployed at organizational entities at Vrije Universiteit Amsterdam: the Industry Alliance Office, and the Demonstrator Lab. These two organizations stimulate science- and business-oriented university technology transfer. In this context, the micro-foundations of the dynamic capabilities "sensing", "seizing" and "reconfiguring" are identified and discussed. For "sensing", which is the university's ability to explore the opportunities in the ecosystem, the micro-foundations are "selecting internal competency" and "sensing external partners". For "seizing", which supports universities in managing complementarity with industry and society, micro-foundations include "resource co-allocation" and "collaborative business model". The micro-foundations of "reconfiguring", through which universities maintain evolutionary fitness in the innovation ecosystem, are "strategic renewal", "establishing a university technology transfer-friendly environment", and "asset orchestration". This study provides researchers with a better understanding of how dynamic capabilities facilitate university technology transfer. Industrial practitioners and policymakers can consider the suggestions of the present study when pursuing collaboration with universities.

Coordinated allocation of water resources and wastewater emission permits based on multi-objective optimization model: From the perspective of conflict between equity and economic benefits

Increasing demand for water resources, shortage of fresh water resources and imperfect management of wastewater emission permits compose the main conflicts of water resource in various basins. Therefore, to alleviate these supply and demand conflicts, watershed managers should search for optimal allocation strategies. Overall, there are two challenges in water resource allocation: the first one is the contradictions among various water sectors exacerbated by existed unfair water resource allocation; the second is the unification of wastewater emission permits and water resource allocation while considering economic, social, and environmental benefits. Based on this, a multi-objective optimization model for united allocation of water resources and wastewater emission licenses was proposed in this paper, which takes into account the maximizing of economic benefits per unit of wastewater emission. Applying this model, the balanced development of economic, social, and environmental benefits can be assured, and the equity of water resource allocation to alleviate the supply-demand contradiction can be maximized. An improved particle swarm optimization algorithm was designed to solve the nonlinear programming model and simulate the cooperative allocation scheme of water resources and wastewater emission permits under different preferences. The case study showed that the proposed model is realistic and sensible, and that watershed managers' preferences has an influence on the coordinated allocation strategy of water resources and wastewater emission permits. For example, the economic benefit differential between the basic trade-off scheme (BTS) and the economic benefit maximization scheme (EBM) is 95.39717 billion yuan, and the Gini coefficient between the BTS and the EMS falls from 0.3874 to 0.3627. The proposed model can successfully balance equity and economic benefits, and provide a reference for the construction of wastewater emission and water resource co-allocation systems.

EVRC: an economic-based virtual resource co-allocation middleware for clouds

In cloud platforms, resource co-allocation service plays an important role on meeting user's requirements especially for running some large-scale applications, which typically need to be deployed on several virtual organisations. In this paper, we present an economic-based virtual resource co-allocation (EVRC) middleware, which provides a set of flexible services that allows cloud applications co-allocating plenty of virtual resources across different resource providers. In the EVRC framework, resource co-allocation service is implemented by a novel auction mechanism, and cloud user's quality-of-service (QoS) requirements are guaranteed by a set of services including resource reservation, reputation manager and etc. In addition, the proposed EVRC middleware is incorporated with an online performance monitoring and profiling mechanism, which can be used to evaluate the efficiency of underlying resources and adjust up-level resource management policy. Extensive experiments are conducted to investigate the effectiveness of the EVRC middleware, and the results indicate that it can significantly improve the efficiency of virtual resource co-allocation as well as cloud user's QoS satisfactory.

EVRC: an economic-based virtual resource co-allocation middleware for clouds

EVRC: an economic-based virtual resource co-allocation middleware for clouds

A Resource Co-Allocation method for load-balance scheduling over big data platforms

Recently, an increasingly large number of high-performance computing (HPC) applications have been developed and hosted in clouds. The cloud computing scheme provides a model of utility computing, which is often implemented in the form of big data centers. As HPC applications are usually resource-intensive, how to exploit economies of scale to harmonize the resource allocation among large-scale HPC applications under performance constraints often poses a significant challenge on the load-balance scheduling policies in big data platforms. In view of this challenge, in this paper we propose a Resource C o-Allocation method, named RCA, for load-balance scheduling. Technically, the method is promoted by four steps that are enacted in a hierarchical control framework. The effectiveness and efficiency of the proposed method are validated by extensive experiments.

An Integrated Resource Co-allocation Middleware for Virtualized Cloud Platforms

An Integrated Resource Co-allocation Middleware for Virtualized Cloud Platforms

Distributed meta-scheduling in lambda grids by means of Ant Colony Optimization

Data transport infrastructures based on wavelength-routed optical networks are appealing to the grid community because they can provide a reconfigurable and high-bandwidth network service to emerging data-intensive grid applications. Traditionally, the control plane of those optical networks is independent from the management of other resources, such as computing systems, and it is commonly limited to fulfill immediate reservation demands of lightpaths. However, the lack of network integration to grid resources and the absence of advance reservation of bandwidth are critical aspects to the widespread adoption of optical networks in grid environments. Therefore, in this work, a distributed grid meta-scheduler based on an Ant Colony Optimization (ACO) algorithm is proposed, which is capable of co-allocating both optical networking and grid resources, under an integrated, extended and distributed control plane that supports advance reservations. The blocking probability and the delay for starting the processing of a request are evaluated for the proposed meta-scheduler under different resource co-allocation algorithms and different meta- and local scheduling policies, including the influence of the information aggregation across grid nodes. In addition, the benefits of the proposed approach are shown, such as grid and networking resource integration at the control plane, and capital expenditure reductions at the deployed optical network when compared to an immediate reservation scenario.

Advance reservation, co-allocation and pricing of network and computational resources in grids

Through the introduction of economic principles, a Resource Management System (RMS) can incorporate user valuations and externalities such as the usage cost of resources, into its scheduling logic. Consequently, an economic RMS can extract more value from the available infrastructure compared to traditional RMSs. In order to use the available infrastructure efficiently, a grid RMS must take an application’s data requirements into account. Although RMSs exist that support the co-allocation and advance reservation of both network and computational resources, they do not incorporate economic principles. In this paper we present ENARA, an economic RMS with advance reservation and co-allocation support for both network and computational resources. The RMS both allocates and prices resources in line with the demand and supply conditions in the network. Through the use of a novel algorithm that determines the limiting links within a network, the RMS is able to estimate prices of individual network links at the start of its planning phase. This resolves an interdependency issue between resource pricing and allocation. We demonstrate that ENARA is able to align resource allocations with user valuations, significantly increasing the user value generated compared to an online approach, while attaining high utilization levels of the infrastructure.

Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

In large-scale grid systems, plenty of applications are constrained by soft or hard deadline requirement. However, it is difficult to guarantee the deadline requirements of these applications because of the dynamical nature of distributed systems. In this paper, a novel approach is proposed to evaluate the deadline-guarantee of co-allocation schemes that obtained from conventional co-allocation policies. Based on this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model integrates multiple co-allocation policies to generate different co-allocation schemes, and selects the one with optimal deadline-guarantee for grid applications. By this way, the hybrid-policy model combines the merits of different policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide coallocation scheme with enhanced deadline-guarantee as well as lower deadline-miss rate.

An Elastic Scheduling Algorithm For Resource Co-Allocation Based on System Generated Predictions With Priority

Resource Co-Allocation is basically used to execute multiple site jobs in a large scale computing environments with secure, faultless and in transparent manner. To be precise we are actually allocating multiple resources for different jobs taking into account the time parameter. Here we make use of the Scheduling queue and Resource Co-Allocation to reduce the Turn-around time with an advanced concept of System Generated Prediction based on Priority. In existing works we are scheduling the resource co-allocation request from user runtime estimation. As user runtime estimations are usually very imprecise that is not clear. In proposed work we are scheduling the resource co-allocation request based on system generated predictions through Discovery service & Priority (fairness and user experience) through topological sorting technique. The system generated predictions are better parameters than user runtime estimates for Resource co-Allocation scheduling, because System generated predictions reduce the scheduling time through proxy ser based discovery service technique. The proposed work consider priorities like advanced reservation, system Generated Predictions, Negotiation, Co-scheduling, policy (SLA, Price, Trust) for resource Co-Allocation. The system generated predictions are better than user runtime estimates for Resource co- Allocation scheduling, using the experimental data’s we proved this concept. End User doesn’t want the grid and resource knowledge only submit job to the portal. This proposed portal will take care of all knowledge about the resource collocation automatically with fast and efficient manner.

INJECT AN ELASTIC GRID COMPUTING TECHNIQUES TO OPTIMAL RESOURCE MANAGEMENT TECHNIQUE OPERATIONS

Evaluation of sharing on the Internet well- develop ed from energetic technique of grid computing. Dyna mic Grid Computing is Resource sharing in large level h igh performance computing networks at worldwide. Existing systems have a Limited innovation for reso urce management process. In proposed work, Grid Computing is an Internet based computing for Optimal Resource Management Technique Operations (ORMTO). ORMTO are Elastic scheduling algorithm, finding the Best Grid node for a task prediction, Fault tolerance resource selection, Perfect resourc e co-allocation, Grid balanced Resource matchmaking and Agent based grid service, wireless mobility resourc e access. Survey the various resource management techniques based on the performance measurement factors like time complexity, Space complexity and Energy complexity find the ORMTO with Grid computing. Objectives of ORMTO will provide an efficient Resource co-allocation automatically for a user who is submitting the job without grid knowledge, desi gn a Grid service (portal) for selects the Best Fault to lerant Resource for a given task in a fast, secure and efficient manner and provide an Enhanced grid balancing system for multi-tasking via Hybrid topology based Grid Ranking. Best Quality of Service (QOS) parameters are important role in all RMT. Proposed system ORMTO use the greater number of QOS Parameters for better enhancement of existing RMT. In proposed system, follow the enhanced techniques and algorithms use to improve the Grid based ORMTO.

Resource co-allocation framework based on hybrid gaming model in grid environments

Resource co-allocation is a fundamental infrastructure to aggregate heterogeneous and distributed resources in grid environments. Although it has been widely investigated, co-allocation under multiple QoS constraints still remains an open issue. To address the issue of resource co-allocation with constraints to budget and deadline, a game-based co-allocation framework is proposed. This framework optimises resources deployment and prices scheme by a three-side co-allocation mechanism; meanwhile, it provides quantitative deadline guarantees for applications by applying queuing system to model the working of grid resources. The validity and solutions of the framework are presented theoretically. Experimental results show that the proposed framework can significantly reduce the deadline missing rate and resource costs for those applications with constraints to cost and deadline.

Three-side Gaming Model for Resource Co-allocation in Grid Computing

Co-allocation is a fundamental infrastructure to aggregate heterogeneous and distributed resources in grid environments. Although it has been studied extensively, co-allocation under the constraints to budget and deadline still remains an opening issue, which means that tradeoff between user QoS requirements and system performance should be agreed. In this paper, a novel agent-based two-phase co-allocation is proposed, which optimizes resources deployment and price scheme through a two-phase co-allocation mechanism, and applies queuing system to model the working of resources for providing quantitative guarantee for application’s deadline requirement. Extensive simulations are conducted to evaluate the effectiveness and performance of the model by comparing with other three co-allocation policies in terms of deadline violation rate, resource benefits and utilization. Experimental results show that the two-phase model can significantly improve the QoS satisfaction for those grid applications with constraints to budget and deadline.

Hybrid-Policy Co-allocation Model in Computational Grid

To address the issue of resource co-allocation with deadline constraint in grids, a novel approach is proposed to evaluate the deadline-guarantee of co-allocation schemes that obtained from conventional co-allocation policies. Based on this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model combines multiple co-allocation policies and selects the one with optimal deadline-guarantee for scheduling. In this way, the hybrid-policy model combines the merits of different policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide co-allocation scheme with improved deadline-guarantee and lower down the deadline-miss rate for real-time applications.

A Survey of Advance Reservation Routing and Wavelength Assignment in Wavelength-Routed WDM Networks

Traditionally, research on routing and wavelength assignment over wavelength-routed WDM networks is concerned with immediate reservation (IR) demands. An IR demand typically does not specify a holding time for data transmission and the start time of the data transmission is assumed to be immediate (i.e. when the connection request arrives). The concept of advance reservation (AR) has recently been gaining attention for optical networks. An AR demand typically specifies information about the start of the data transmission or a deadline, as well as the holding time of the transmission. AR has several important applications for both wide-area networks and Grid networks. For example, AR can be used for adjusting virtual topologies to adapt to predictable peak hour traffic usage. It can be used to provide high-bandwidth services such as video conferencing and in Grid applications requiring the scheduled distribution of large files and for co-allocation of network and grid resources. AR can also be beneficial to the network by allowing the network operator to better plan resource usage and therefore increase utilization. Knowledge of the holding time can lead to more optimal decisions for resource allocation. This translates to better quality of service for users. In this paper we provide a comprehensive survey of the past and current work on advance reservation for optical networks. There have been many variations of the advance reservation concept proposed, so we will also provide a broad classification. In addition to the survey, we will discuss what we believe are important areas of future work and open challenges for advance reservation on optical networks.

Hierarchical scheduling strategies for parallel tasks and advance reservations in grids

Recently, the advance reservation functionality gained high importance in grids due to increasing popularity of modern applications that require interactive tasks, co-allocation of multiple resources, and performance guarantees. However, simultaneous scheduling, both advance reservations and batch tasks affects the performance. Advance reservations significantly deteriorate flow time of batch tasks and the overall resource utilization, especially in hierarchical scheduling structures. This is a consequence of unknown batch task processing times and the lack of possibility of altering allocations of advance reservations. To address these issues we present a common model for scheduling both computational batch tasks and tasks with advance reservation requests. We propose simple on-line scheduling policies and generic advices that reduce negative impact of advance reservations on a schedule quality. We also propose novel data structures and algorithms for efficient scheduling of advance reservations. A comprehensive experimental analysis is presented to show the influence of advance reservations on resource utilization, mean flow time, and mean tardiness—the criteria significant for administrators, users submitting batch tasks, and users requesting advance reservations, respectively. All experiments were performed with a well-known real workload using the GSSIM simulator.

Use of run time predictions for automatic co-allocation of multi-cluster resources for iterative parallel applications

Metaschedulers co-allocate resources by requesting a fixed number of processors and usage time for each cluster. These static requests, defined by users, limit the initial scheduling and prevent rescheduling of applications to other resource sets. It is also difficult for users to estimate application execution times, especially on heterogeneous environments. To overcome these problems, metaschedulers can use performance predictions for automatic resource selection. This paper proposes a resource co-allocation technique with rescheduling support based on performance predictions for multi-cluster iterative parallel applications. Iterative applications have been used to solve a variety of problems in science and engineering, including large-scale computations based on the asynchronous model more recently. We performed experiments using an iterative parallel application, which consists of benchmark multiobjective problems, with both synchronous and asynchronous communication models on Grid’5000. The results show run time predictions with an average error of 7% and prevention of up to 35% and 57% of run time overestimations to support rescheduling for synchronous and asynchronous models, respectively. The performance predictions require no application source code access. One of the main findings is that as the asynchronous model masks communication and computation, it requires no network information to predict execution times. By using our co-allocation technique, metaschedulers become responsible for run time predictions, process mapping, and application rescheduling; releasing the user from these burden tasks.

Resource Selection Algorithms for Economic Scheduling in Distributed Systems

In this paper, we present slot selection algorithms in economic models for independent job batch scheduling in distributed computing with non-dedicated resources. Existing approaches towards resource co-allocation and multiprocessor job scheduling in economic models of distributed computing are based on search of time-slots in resource occupancy schedules. The sought time-slots must match requirements of necessary span, computational resource properties, and cost. Usually such scheduling methods consider only one suited variant of time-slot set. This paper discloses a scheduling scheme that features multi-variant search. Two algorithms of linear complexity for search of alternative variants are compared. Having several optional resource configurations for each job makes an opportunity to perform an optimization of execution of the whole batch of jobs and to increase overall efficiency of scheduling.

Grid Resource Negotiation: Survey and New Directions

Since Grid computing systems involve large-scale resource sharing, resource management is central to their operations. Whereas there are more Grid resource management systems adopting auction, commodity market, and contract-net (tendering) models, this survey supplements and complements existing surveys by reviewing, comparing, and highlighting existing research initiatives on applying bargaining (negotiation) as a mechanism to Grid resource management. The contributions of this paper are: 1) discussing the motivations for considering bargaining models for Grid resource allocation; 2) discussing essential design considerations such as modeling devaluation of Grid resources, considering market dynamics, relaxing bargaining terms, and co-allocation of resources when building Grid negotiation mechanisms; 3) reviewing the strategies and protocols of state-of-the-art Grid negotiation mechanisms; 4) providing detailed comparisons and analyses on how state-of-the-art Grid negotiation mechanisms address the design considerations mentioned in 3); and 5) suggesting possible new directions.

An Improved Protocol for Deadlock and Livelock Avoidance Resource Co-allocation in Network Computing

A multitude of applications require simultaneous access to multiple kinds of resources scatted in distributed sites. This problem is known as resource co-allocation which has evolved as a hot topic in network computing. How to design a kind of high-performance protocol for deadlock and livelock avoidance resource co-allocation becomes a challenging problem. In this paper, we propose a new protocol OODP3 (Optimal ODP3) which is based on the currently popular protocol ODP3 (Order-based Deadlock Prevention Protocol with Parallel requests).OODP3 not only inherits the advantage of ODP3 but also guarantees the fulfillment of resource co-allocation within polynomial time. Theoretical proof is conducted to verify the correctness of OODP3. Experimental results also show that OODP3 achieves the better performance improvements than the existing deadlock and livelock avoidance protocol.