CAPM Indexed Hybrid E-Negotiation for Resource Allocation in Grid Computing

2013 ◽  
Vol 5 (2) ◽  
pp. 72-91 ◽  
Author(s):  
Ashiqur Md. Rahman ◽  
Rashedur M Rahman
Keyword(s):  
Resource Allocation ◽  
Grid Computing ◽  
Dynamic Pricing ◽  
Large Scale ◽  
Service Providers ◽  
Computational Grids ◽  
Grid Environment ◽  
Expected Return ◽  
Grid Systems ◽  
Equilibrium Relationship

Computational Grids are a promising platform for executing large-scale resource intensive applications. This paper identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economy-based Grid systems, and proposes an economy based negotiation system protocol for cooperative and competitive trading of resources. Dynamic pricing for services and good level of Pareto optimality make auctions more attractive for resource allocation over other economic models. In a complex Grid environment, the communication demand can become a bottleneck; that is, a number of messages need to be exchanged for matching suitable service providers and consumers. The Fuzzy Trust integrated hybrid Capital Asset Pricing Model (CAPM) shows the higher user centric satisfaction and provides the equilibrium relationship between the expected return and risk on investments. This paper also presents an analysis on the communication requirements and the necessity of the CAPMAuction in Grid environment.

scholarly journals DRIVE: A Distributed Economic  Meta-Scheduler for the Federation  of Grid and Cloud Systems

2021 ◽  
Author(s):  
◽  
Kyle Chard
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Large Scale ◽  
Service Providers ◽  
Distributed Environment ◽  
Resource Managers ◽  
Virtual Organisation ◽  
Open Service ◽  
Social Cloud ◽  
Computing Infrastructure

<p>The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains.</p>

SCHEDULING WITH JOB CHECKPOINT IN COMPUTATIONAL GRID ENVIRONMENT

2011 ◽  
Vol 02 (03) ◽  
pp. 299-316
Author(s):  
MALARVIZHI NANDAGOPAL ◽  
S. GAJALAKSHMI ◽  
V. RHYMEND UTHARIARAJ
Keyword(s):  
Fault Tolerance ◽  
Large Scale ◽  
Job Scheduling ◽  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Computational Grids ◽  
Tolerance Mechanism ◽  
Grid Resource ◽  
Distributed Resources ◽  
Grid Environment

Computational grids have the potential for solving large-scale scientific applications using heterogeneous and geographically distributed resources. In addition to the challenges of managing and scheduling these applications, reliability challenges arise because of the unreliable nature of grid infrastructure. Two major problems that are critical to the effective utilization of computational resources are efficient scheduling of jobs and providing fault tolerance in a reliable manner. This paper addresses these problems by combining the checkpoint replication based fault tolerance mechanism with minimum total time to release (MTTR) job scheduling algorithm. TTR includes the service time of the job, waiting time in the queue, transfer of input and output data to and from the resource. The MTTR algorithm minimizes the response time by selecting a computational resource based on job requirements, job characteristics, and hardware features of the resources. The fault tolerance mechanism used here sets the job checkpoints based on the resource failure rate. If resource failure occurs, the job is restarted from its last successful state using a checkpoint file from another grid resource. Globus ToolKit is used as the grid middleware to set up a grid environment and evaluate the performance of the proposed approach. The monitoring tools Ganglia and Network Weather Service are used to gather hardware and network details, respectively. The experimental results demonstrate that, the proposed approach effectively schedule the grid jobs with fault-tolerant way thereby reduces TTR of the jobs submitted in the grid. Also, it increases the percentage of jobs completed within specified deadline and making the grid trustworthy.

scholarly journals DRIVE: A Distributed Economic  Meta-Scheduler for the Federation  of Grid and Cloud Systems

2021 ◽  
Author(s):  
◽  
Kyle Chard
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Large Scale ◽  
Service Providers ◽  
Distributed Environment ◽  
Resource Managers ◽  
Virtual Organisation ◽  
Open Service ◽  
Social Cloud ◽  
Computing Infrastructure

<p>The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains.</p>

scholarly journals Trust Management for Grid Systems

2010 ◽  
pp. 149-178
Author(s):  
Benjamin Aziz ◽  
Alvaro Arenas ◽  
Fabio Martinelli ◽  
Paolo Mori ◽  
Marinella Petrocchi
Keyword(s):  
Grid Computing ◽  
Trust Management ◽  
Large Scale ◽  
Distributed Computation ◽  
Shared Resources ◽  
Grid Systems ◽  
Grid Middleware ◽  
Grid Environments ◽  
Administrative Domains ◽  
Heterogeneous Resources

Grid computing is a paradigm for distributed computation on shared resources. It uses a large-scale, highly decentralized infrastructure, in which a huge number of participants share heterogeneous resources for a given purpose. Each participant both provides their own resources and exploits others’ resources, combining them to solve their own problems. Trust management is a major issue in the shared Grid environment because Grid participants are usually unknown to each other and usually belong to separate administrative domains, with little or no common trust in the security of opposite infrastructures. The standard security support provided by the most common Grid middleware may be regarded as one means through which such common trust may be established. However, such security solutions are insufficient to exhaustively address all the trust requirements of Grid environments. In this chapter, the authors survey proposals for enhancing trust management in Grid systems.

A Fuzzy Rule-Based Optimisation Model for Efficient Resource Utilisation in a Grid Environment Using Proximity Awareness and Semantic Technology

2018 ◽  
Vol 17 (02) ◽  
pp. 1850023 ◽  
Author(s):  
Abdul Khalique Shaikh ◽  
Saadat M. Alhashmi ◽  
Rajendran Parthiban ◽  
Amril Nazir
Keyword(s):  
Resource Allocation ◽  
Fuzzy Rule ◽  
Resource Discovery ◽  
Computational Grids ◽  
Resource Utilisation ◽  
Grid Environment ◽  
Rule Based ◽  
Efficient Resource ◽  
First Come First Serve ◽  
Fuzzy Techniques

The performance of computational grids mainly depends on the resource allocation service of a resource management system. Efficient resource allocation is essential for better resource utilisation which could be for both providers and grid users. Resource allocation includes the scheduling of gridlets to the available resources. However, the biggest challenges for grid users are to select the best resources from the available grid resources and to allocate these resources for scheduling of the gridlets. To address these issues and enhance the resource utilisation process, we propose a semantic and proximity-aware fuzzy rule-based model that improves the resource utilisation in a grid environment. The model uses fuzzy techniques with four parameters such as semantic similarity, proximity, number of total machines and number of total processors of each machine. The experimental results provide promising results. Overall, the proposed semantic and proximity-aware fuzzy rule-based decentralised resource discovery model improves the resource utilisation by 23% as compared to non-fuzzy first come first serve (FCFS) technique in a computational grid environment.

scholarly journals Cloud based Cost Effective Resource Allocation Model for Software-as-a-service Deployments

2013 ◽  
Vol 9 (2) ◽  
pp. 1068-1079
Author(s):  
Ibrahim A. Cheema ◽  
Mudassar Ahmad ◽  
Fahad Jan ◽  
Shahla Asadi
Keyword(s):  
Resource Allocation ◽  
Large Scale ◽  
Service Providers ◽  
Cost Effective ◽  
Software As A Service ◽  
Allocation Model ◽  
Application Service Providers ◽  
Resource Allocation Model ◽  
Application Service ◽  
Cloud Infrastructures

The Cloud Computing (CC) provides access to the resources with usage based payments model. The application service providers can seamlessly scale the services. In CC infrastructure, a different number of virtual machine instances can be created depending on the application requirements. The capability to scale Software-as-a-Service (SaaS) application is very attractive to the providers because of the potential to scale application resources to up or down, the user only pay for the resources required. Even though the large-scale applications are deployed on cloud infrastructures on pay-per-use basis, the cost of idle resources (memory, CPU) is still charged to application providers. The issues of saturation and wastage of cloud resources are still unresolved. This paper attempts to propose the resource allocation models for SaaS applications deployments over CC platforms. The best balanced resource allocation model is proposed keeping in view cost and user requirements.

scholarly journals Learning-based Resource Allocation for Backscatter-aided Vehicular Networks

2021 ◽  
Author(s):  
Wali Ullah Khan ◽  
Tu N. Nguyen ◽  
Furqan Jameel ◽  
Muhammad Ali Jamshed ◽  
Haris bin Pervaiz ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Spectrum Sharing ◽  
Large Scale ◽  
Vehicular Networks ◽  
Service Providers ◽  
User Association ◽  
Joint Power ◽  
Macro Cell ◽  
Effective Manner ◽  
Multiple Network

This work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner.

scholarly journals Learning-based Resource Allocation for Backscatter-aided Vehicular Networks

2021 ◽  
Author(s):  
Wali Ullah Khan ◽  
Tu N. Nguyen ◽  
Furqan Jameel ◽  
Muhammad Ali Jamshed ◽  
Haris bin Pervaiz ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Spectrum Sharing ◽  
Large Scale ◽  
Vehicular Networks ◽  
Service Providers ◽  
User Association ◽  
Joint Power ◽  
Macro Cell ◽  
Effective Manner ◽  
Multiple Network

This work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner.

Design Models for Resource Allocation in Cyber-Physical Energy Systems

2012 ◽  
pp. 111-130
Author(s):  
Prakash Ranganathan ◽  
Kendall Nygard
Keyword(s):  
Resource Allocation ◽  
Mathematical Models ◽  
Large Scale ◽  
Energy Resources ◽  
Control Mechanisms ◽  
Grid Systems ◽  
Physical Energy ◽  
Smart Control ◽  
Partially Observable

Today’s and tomorrow’s smart grid systems are made more efficient, cleaner, and reliable by “smart” control mechanisms and decision models that deliver information to consumers so they can better manage energy resources. The rapidly changing needs and opportunities of today’s electric grid market require unprecedented levels of interoperability to integrate diverse information systems to share knowledge and collaborate among sub-devices or sub-systems in the grid. This book chapter focuses on optimal mathematical models for resource allocation. A series of mathematical models is presented in this book chapter for solving large-scale energy allocation problems with partially observable states, utility functions, and constrained action is introduced. The authors’ techniques use a Linear Programming (LP) approach to determine resource allocations among a set of fuzzy rules that allocates Distributed Energy Resources (DER’s) or power sources/sinks and uses to determine improving resource management.

Improving Energy-Efficiency of Computational Grids via Scheduling

2011 ◽  
pp. 1836-1857
Author(s):  
Ziliang Zong ◽  
Xiaojun Ruan ◽  
Adam Manzanares ◽  
Kiranmai Bellam ◽  
Xiao Qin
Keyword(s):  
Energy Efficiency ◽  
Energy Conservation ◽  
Grid Computing ◽  
Energy Efficient ◽  
High Speed ◽  
High Performance ◽  
Large Scale ◽  
Rapid Development ◽  
Computational Grids ◽  
Grid Architecture

High performance Grid platforms and parallel computing technologies are experiencing their golden age because of the convergence of four critical momentums: high performance microprocessors, high-speed networks, free middleware tools, and highly increased needs of computing capability. We are witnessing the rapid development of computational Grid technologies. Dozens of exciting Grid infrastructures and projects like Grid-tech, Grid Portals, Grid Fora, and Commercial Grid Initiatives are being built all over the world. However, the fast growing power consumption of data centers has caused serious concerns for building more large-scale supercomputers, clusters, and Grids. Therefore, designing energy-efficient computational Grids to make them economically attractive and environmentally friendly for parallel applications becomes highly desirable. Unfortunately, most previous studies in Grid computing primarily focus on the improvement of performance, security, and reliability, while completely ignoring the energy conservation issue. To address this problem, we propose a general architecture for building energy-efficient computational Grids and discuss the potential possibilities for incorporating power-aware techniques to different layers of the proposed Grid architecture. In this chapter, we first provide necessary background on computational Grids, Grid computing, and parallel scheduling. Next, we illustrate the general Grid architecture and explain the functionality of different layers. Followed by that, we discuss the design and implementation details of applying the energy-efficient job-scheduling technique, which is called Communication Energy Conservation Scheduling (or CECS for short), to computational Grids. Finally, we present extensive simulation results to prove the improvement of energy-efficiency of computational Grids.

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