In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution.
Liang Yu, Tao Jiang, Yang Cao, Shiyong Yang · 5 authors
In Internet Data Center (IDC) operations, some uncertainties are needed to be managed. Otherwise, IDC operators will be faced with a high operation risk. In existing work, electricity forward contract is adopted to minimize the IDC operation risk in smart grid environment under the assumption that workload and spot electricity price are independent. However, the above assumption is unreasonable in smart grid environment, resulting in the unsatisfactory performance of electricity forward contract. In this paper, a novel scheme is proposed to minimize the IDC operation risk in smart grid environment considering the correlation between workload and spot electricity price, based on a portfolio consisting of electricity forward contracts and electricity call (put) options in electricity derivative markets. Simulation results show that the proposed scheme can significantly reduce the IDC operation risk. Compared with electricity forward contract, the proposed scheme has more stable and better performance.
Jun 1, 2012·2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems
Bogdan Florin Cornea, Julien Bourgeois, The Tung Nguyen, Didier El Baz
Recently, a new environment for high performance peer-to-peer distributed computing was proposed. This environment, named P2PDC, addresses stable or volatile systems communicating in a decentralized manner using the self-adaptive protocol P2PSAP. P2PDC is devoted to task parallel applications like numerical simulation problems or optimization problems solved via parallel or distributed iterative algorithms. For distributed applications meant to run with P2PDC, a performance prediction tool named dPerf was proposed. dPerf combines static and dynamic analysis with trace-based simulation to provide scientist with information about the execution of their large scale numerical simulation applications. dPerf addresses real parallel and distributed numerical simulation and optimisation applications written in C, C++ or Fortran for P2PDC. This paper introduces an enhancement of the dPerf tool which provides scalable performance prediction results. Scaling is done with respect to (i) network configuration and (ii) number of peers. Scaling predictions based on network configuration is achieved through trace-based simulation, where various architectures can be studied. Scaling predictions based on the number of peers implies analyzing the communication topology and modifying trace files prior to simulation. We present experimental results obtained for the obstacle problem, a C/P2PDC implementation of the code used in mechanics and finance. Prediction for this application is computed under real conditions, with a reduced slowdown and by providing user with scalable results.
We present a general framework for constructing non-interactive universally composable (UC) commitment schemes that are secure against adaptive adversaries in the non-erasure setting under a single re-usable common reference string. Previously, such âfully-equipped â UC commitment schemes are only known in [8, 9], with an unavoidable overhead of O(Îș) in the sense of communication and computational complexities; meaning that to commit λ bits, the communication and computational costs require O(λÎș), where Îș denotes the security parameter. Efficient construction of a fully-equipped UC commitment scheme was a long-standing open problem. We introduce a cryptographic primitive, called all-but-many encryptions (ABMEs), and prove that it is a translation of fully-equipped UC commitment in the primitive level. We then construct ABMEs from cryptographic primitives that we call a probabilistic pseudo random function family and extractable sigma protocols â the former is a probabilistic version of a pseudo random function family and the latter is a special kind of sigma (i.e., canonical 3-round public-coin HVSZK) protocols with some extractability. We provide fully-equipped UC commitment schemes from ABMEs under DDH and DCR-based assumptions, respectively. In particular, the DCR-based scheme is the first fully-equipped UC commitment scheme with optimal expansion factor âŠ(1); to commit Îș bits, the communication and computational costs are âŠ(Îș). We further construct a fully-equipped UC commitment scheme from a general assumption (in which trap-door permutations exist), which is far more efficient than the previous construction [9], because, unlike [9], our construction does not require non-interactive zero-knowledge proof systems. 1
Welcome to the special issue of Concurrency and Computation: Practice and Experience (CCPE) journal. This special issue compiles a number of excellent technical contributions that significantly advance the state-of-the-art in autonomic cloud computing. Cloud computing 1, 2 is an emerging utility computing model that allows users to dynamically access, select, and configure a large pool of IT resources (virtual machine templates, storage, and networking elements) and deliver them as âcomputing utilitiesâ to consumers in a pay-as-you-go manner. Several vendors have emerged in this space including IBM, VMware, Microsoft, Manjrasoft, and Yahoo. This model of computing is quite attractive, especially for small and medium sized enterprises, as it allows them to focus on consuming or offering services on top of cloud infrastructure. At high-level, cloud computing might not seem radically different from the existing paradigms: World Wide Web, grid computing, and cluster computing. However, key differentiators of cloud computing are its technical characteristics such as on-demand resource pooling or rapid elasticity, self-service, almost infinite scalability, end-to-end virtualization support, and robust support of resource usage metering and billing. Additionally, nontechnical differentiators include services that are offered under pay-as-you-go-model, guaranteed Service Level Agreement (SLA), faster time to deployments, lower upfront costs, little or no maintenance overhead, and environment friendliness. Unpredictability is a fact in a distributed computing environment, and the Cloud is no exception. Performance unpredictability 3 in the Cloud is in fact a major issue for many users and it is coined as one of the major obstacles for cloud computing. For instance, researchers (biologists, physicists, finance analysts, etc.) expect guaranteed performance for their experiments, independent of the current workload and state 4 of IT resources of the Cloud, because this is key to repeatability of results. Other examples are small and medium sized enterprises (gaming company, web application providers) that want strict assurance on SLA; for example, an end-user request for a web page or multimedia content has to be served within the agreed time-limit. Hence, it is highly important for Cloud vendors that they have the ability to offer guaranteed SLAs based on performance metrics â such as response time and throughput. Interestingly, vendors seem to base their SLAs on availability of their offering, while completely ignoring response time and throughput. Hence, it is clear that dealing with performance unpredictability is critical to exploiting the full potential of clouds. In this special issue, we have tried to compile some high quality papers that exhaustively deal with some of the aforementioned issues. Next, we briefly describe the technical contributions, which were selected for publication in this special issue. All of the selected papers underwent a rigorous peer-review process. The end-to-end QoS negotiation for SLA establishment for composite services involves compound multiparty negotiations in which the composite service provider concurrently negotiates with multiple candidates for each atomic service, selecting the one that best satisfies the atomic service QoS preferences while ensuring that the end-to-end QoS requirements are also fulfilled. It is necessary to derive the atomic utility boundaries from the global utility boundary to be able to negotiate with potential candidates. Additionally, there has to be a mechanism for updating these boundaries in subsequent negotiation rounds based upon the individual negotiation outcomes. To counter these complexities, in paper 5 titled âEstablishing Composite SLAs through Concurrent QoS Negotiation with Surplus Redistributionâ, Richter et al. propose an algorithm for the decomposition of global utility boundary into atomic service utility boundaries, and the surplus redistribution from successful negotiation outcomes among the remaining negotiations. The proposed mechanism is a practical approach to efficiently coordinate concurrent service negotiations within complex workflows, enabling the iterative and interactive adjustment of the negotiation boundaries for each atomic service in a composition based on the performance of other atomic negotiations. They demonstrate the feasibility of our approach by evaluating it with some popular negotiation strategies using the Specialised Property Search Scenario. Many scientific workflows are data intensive where large volumes of intermediate data are generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity determined manually. As doing science in the Cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay-for-use model. In the paper in 6 titled âA data dependency based strategy for intermediate data storage in scientific cloud workflow systemsâ, Yuan et al. build an intermediate data dependency graph (IDG) from the data provenance in scientific workflows. With the IDG, deleted intermediate data can be regenerated, and as such they develop a novel intermediate data storage strategy that can reduce the cost of scientific cloud workflow systems by automatically storing appropriate intermediate data sets with one Cloud service provider. The strategy has significant research merits, that is, it achieves a cost-effective trade-off of computation cost and storage cost and is not strongly impacted by the forecasting inaccuracy of data setsâ usages. Meanwhile, the strategy also takes the usersâ tolerance of data accessing delay into consideration. Authors utilize Amazon's cost model and apply the strategy to general random and specific astrophysics pulsar searching scientific workflows for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly. Recall that, one of the biggest premises of cloud computing is the flexibility of delivering IT resources and virtual appliances as an utility such as phone, electricity, gas, and water services. It enables users to have access to computing infrastructure, platform, and software as services over the Internet. To be competitive, however, Cloud providers need to be able to adapt to the dynamic loads from users, not only optimizing the local usage and costs but also engaging into agreements with other clouds to complement local capacity. The infrastructure in which competing clouds are able to cooperate to maximize their benefits is called a Federated Cloud. Just as clouds enable users to cope with unexpected demand loads, a Federated Cloud will enable individual clouds to cope with unforeseen variations of demand. The definition of the mechanism to ensure mutual benefits for the individual clouds composing the federation, however, is one of its main challenges. Gomes et al. in their paper 7 âPure exchange markets for resource sharing in federated cloudsâ propose and investigate the application of market-oriented mechanisms based on the General Equilibrium Theory of Microeconomics to coordinate the sharing of resources between the clouds in a Federated Cloud. Several research institutions and universities own computational capacity that is not effectively utilized, thereby providing an opportunity for such institutions to use such capacity to offer Cloud services (to both internal and external users). However, the unreliability and unpredictability of these resources mean that their use in the context of an SLA is high risk, leading to a reduction in reputation and economic penalties in case of SLA violation. To overcome these challenges, in the paper 8 titled âTowards autonomic management for Cloud services based upon volunteered resourcesâ, Caton and Rana propose a methodology that addresses the issues of unreliability and unpredictability such that Cloud software services could be hosted upon volunteered resources. To enable the harnessing of these resources, they rely on autonomic fault management techniques that allow such systems to independently adapt to the resources they use based upon their perception of individual resource reliability. Using the proposed approach they were able to scale out the backend infrastructure of the Cloud service elastically (minimum 30 s per worker), opportunistically, and autonomically. To summarize, the authors address two key questions in their paper: Can a campus volunteer infrastructure be used in Cloud provisioning? and What measures are necessary to ensure reliability at the resource level? To improve the hosting and delivery of applications through cloud-based IT resources, Champrasert et al. in the paper 9 titled âExploring self-optimization and self-stabilization properties in bio-inspired autonomic cloud computingâ, describe architecture to build self-optimizable and self-stabilizable applications. The design of the proposed architecture, SymbioticSphere, is inspired by key biological principles such as decentralization, evolution, and symbiosis. In SymbioticSphere, each cloud application consists of application services and middleware platforms. Each service and platform is designed as a biological entity, and implements biological behaviors such as energy exchange, migration, reproduction, and death. Each service/platform possesses behavior policies, as genes, each of which defines when and how to invoke a particular behavior. SymbioticSphere allows services and platforms to autonomously adapt to dynamic network conditions by optimizing their behavior policies with a multi-objective genetic algorithm. Moreover, SymbioticSphere allows services and platforms to autonomously seek stable adaptation decisions as equilibria (or symbiosis) between them with a game theoretic algorithm. This symbiosis augments evolutionary optimization to expedite the adaptation of agents and platforms. It also contributes to stable performance that contains a very limited amount of fluctuations. Simulation results demonstrate that agents and platforms successfully attain self-optimization and self-stabilization properties in their adaptation processes. We hope that the readers will find the articles of this special issue to be informative and useful.
Sonu Mariam Paulose, R. Venkatesan, K. Ramalakshmi
Conjunction of massive amount of idle computers or resources that may be loosely coupled, heterogeneous and geographically dispersed to reach a common goal leads to a virtual computing platform for sharing resources across the world. Resource management, application development and usage models in these environments has some dilemma in undertaking resources due to resource providers with multiple administrative domains having their own policies and terms. A ditch in the Grid computing environment is how to coordinate the distributed resources amongst a dynamic set of individuals and organizations where the requesters and providers are allowed to join and leave Grid environment at any time. Bidding model prevents single point of failure and server overload problems of match making model while minimizing turnaround time by using some set of deterministic and probabilistic selection heuristics where resource requesters and resource providers were given the privilege to take autonomous decisions regarding resource selection. Autonomous decision making is enabled via peer-to-peer decentralized scheduling frame work. In decentralized environment, lack of global information is a key challenge to facilitate optimum decision making which can lead to greedy selection of the best provider. Therefore some probabilistic selection is used to reduce the fairness deviation among processors while minimizing the turnaround time. Currently just various level of information about providers has been concentrated to minimize the turnaround time. Simply concentrating on various level of information may also leads to failure due to rejection factor resulted by number of failures occurred at provider. However by merging trust oriented mechanisms along with various level of information, rejection factor can also be minimized along with minimization of the turnaround time.
Stelios Sotiriadis, Nik Bessis, Ye Huang, Paul Sant · 5 authors
Grid technology enables resource sharing among a massive number of dynamic and geographically distributed resources. The significance of such environments is based on the aptitude of grid members to look across multiple grids for resource discovery and allocation. Parallel to grid, agents are autonomous problem solvers capable of self-directed actions in flexible environments. As grid systems require self-sufficiency, agents may be the means by which to achieve a robust autonomy infrastructure. In this direction we propose a resource discovery method of interoperable grid agents which travel within Virtual Organizations (VOs) and by capturing resource information regarding their action domain; they update the internal data of each grid member. Moreover we propose that resource discovery is a systematic and continually updating process that occurs within a VO and allows information exchange to happen. This exchange takes place between various community members at a pre-defined interval, aiming to distribute internal knowledge about the domain.
This chapter presents a clustered peer-to-peer system as a resource organization structure for web-service hosting platforms. Where service quality, such as response time and service availability, are provided with assurance. The peer-to-peer organization allows integration of autonomous resources into a single platform in a scalable manner. In clustered peer-to-peer systems, nodes are organized into clusters based on some proximity metric, and a distributed hash table overlay is created among the clusters. This organization enables lightweight techniques for load balancing among different clusters, which is found to be essential for providing response time guarantees. Service availability is provided by replicating a service instance in multiple nodes in a cluster. A decentralized load balancing technique called diffusive load balancing is presented in the context of clustered peer-to-peer systems and evaluated for effectiveness and performance.
To increase the flexibility of proxy divisible E-cash system and decrease its communication and computational complexity,and to revoke proxy power after finite warrant period,a new proxy divisible E-cash system based on discrete logarithm proxy signature and discrete logarithm zero knowledge proof is presented.The new scheme satisfies non-forgeability,nonrepeatability,coin tracing and owner tracing that common E-cash possess security requirement.Finally,compared with divisible E-cash system based on 2-node tree partition method,the study shows that the new scheme is based simple subtration partition method and has high efficiency and low communication.
Agostino Forestiero, Carlo Mastroianni, Giandomenico Spezzano
This article presents So-Grid, a set of bio-inspired algorithms tailored to the decentralized construction of a Grid information system that features adaptive and self-organization characteristics. Such algorithms exploit the properties of swarm systems, in which a number of entities/agents perform simple operations at the local level, but together engender an advanced form of swarm intelligence at the global level. In particular, So-Grid provides two main functionalities: logical reorganization of resources, inspired by the behavior of some species of ants and termites that move and collect items within their environment, and resource discovery, inspired by the mechanisms through which ants searching for food sources are able to follow the pheromone traces left by other ants. These functionalities are correlated, since an intelligent dissemination can facilitate discovery. In the Grid environment, a number of ant-like agents autonomously travel the Grid through P2P interconnections and use biased probability functions to: (i) replicate resource descriptors in order to favor resource discovery; (ii) collect resource descriptors with similar characteristics in nearby Grid hosts; (iii) foster the dissemination of descriptors corresponding to fresh (recently updated) resources and to resources having high quality of service (QoS) characteristics. Simulation analysis shows that the So-Grid replication algorithm is capable of reducing the entropy of the system and efficiently disseminating content. Moreover, as descriptors are progressively reorganized and replicated, the So-Grid discovery algorithm allows users to reach Grid hosts that store information about a larger number of useful resources in a shorter amount of time. The proposed approach features characteristics, including self-organization, scalability and adaptivity, which make it useful for a dynamic and partially unreliable distributed system.
Richard Sinnott, David Chadwick, T. Doherty, David MartĂn · 8 authors
Grids allow for collaborative e-Research to be undertaken, often across institutional and national boundaries. Typically this is through the establishment of virtual organizations (VOs) where policies on access and usage of resources across partner sites are defined and subsequently enforced. For many VOs, these agreements have been lightweight and erred on the side of flexibility with minimal constraints on the kinds of jobs a user is allowed to run or the amount of resources that can be consumed. For many new domains such as e-Health, such flexibility is simply not tenable. Instead, precise definitions of what jobs can be run, and what data can be accessed by who need to be defined and enforced by sites. The role based access control model (KBAC) provides a well researched paradigm for controlling access to large scale dynamic VOs. However, the standard RBAC model assumes a single domain with centralised role management. When RBAC is applied to VOs, it does not specify how or where roles should be defined or made known to the distributed resource sites (who are always deemed to be autonomous to make access control decisions). Two main possibilities exist based on either a centralized or decentralized approach to VO role management. We present the advantages and disadvantages of the centralized and decentralized role models and describe how we have implemented them in a range of security focused e-Research domains at the National e-Science Centre (NeSC) at the University of Glasgow.
Ali Rezaee, Amir Masoud Rahmani, Saeed P arsa, Sahar Adabi
Grid computing is emerged as a new distributed computing technology that implements flexible and coordinated resource sharing among dynamic, heterogeneous, unpredictable and geographically distributed collections of resources owned by different individuals and organizations. Due to heterogeneous and dynamic nature of the grid, resource management and application scheduling is a complex undertaking. This paper proposed a multi-agent architecture that addressed resource management and application execution with support for Quality of Services (QoS) in grid environment. Five types of collaborative intelligent and mobile agents proposed to manage grid resources and applications in a decentralized, autonomous and intelligent manner. In the proposed architecture, negotiation, advanced reservation and QoS measurements handled by collaborative intelligent agents. Resources and applications are managed independently with respect to their defined policies. The simulation results showed that the multi-agent architecture is practical, flexible and effective.
Recently, there have been considerable efforts towards the convergence between P2P and Grid computing in order to reach a solution that takes the best of both worlds by exploiting the advantages that each offers. Augmenting the peer-to-peer model to the services of the Grid promises to eliminate bottlenecks and ensure greater scalability, availability, and fault-tolerance. The Grid Information Service (GIS) directly influences quality of service for grid platforms. Most of the proposed solutions for decentralizing the GIS are based on completely flat overlays. The main contributions for this paper are: the investigation of a novel resource discovery framework for Grid implementations based on a hierarchy of structured peer-to-peer overlay networks, and introducing a discovery algorithm utilizing the proposed framework. Validation of the framework-s performance is done via simulation. Experimental results show that the proposed organization has the advantage of being scalable while providing fault-isolation, effective bandwidth utilization, and hierarchical access control. In addition, it will lead to a reliable, guaranteed sub-linear search which returns results within a bounded interval of time and with a smaller amount of generated traffic within each domain.
Computational grids enable the sharing, aggregation, and selection of (geographically distributed) computational resources and can be used for solving large scale and data intensive computing applications. Computational grids are an appealing target application for market-based resource allocation especially given the attention in recent years to âvirtual organizations â and policy requirements. In this paper, we present a framework for truthful, decentralized, dynamic auctions in computational grids. Rather than a fullyspecified auction, we propose an open, extensible framework that is sufficient to promote simple, truthful bidding by endusers while supporting distributed and autonomous control by resource owners. Our auction framework incorporates resource prediction in enabling an expressive language for end-users, and highlights the role of infrastructure in enforcing rules that balance the goal of simplicity for end users with autonomy for resource owners. The technical analysis leverages simplifying assumptions of âuniform failureâ and âthreshold-reliabilityâ beliefs.
Fundamental challenges in designing environments with media-rich ambient services involves not only the development of appropriate sensing technologies, but as importantly, the implementation of a distributed media processing system which can process, integrate, and leverage the sensed data in real time to provide the various services. In recent years, a great deal of progress has been made in media service workflow processing systems. In most existing solutions, however, the workflow nodes, which operate on the data, are selected out of a centrally assigned candidate pool. These candidate organizations cause either extensive resource provisioning or poor-quality operator mapping between logical workflow nodes and the available physical resources nodes. Consequently, instantiating a media processing workflow to the underlying hardware before the workflow execution begins does not lends itself to adaptive and autonomous operation of the workflow, scalable to resources and demand.In this paper, we propose a novel decentralized multimedia workflow processing system, DANS, in which operators defined in workflows are mapped into (distributed) physical nodes through Distributed Hash Table (DHT)-based overlay substrate in a purely decentralized and adaptive manner. The redundancy in the system, in terms of availability of multiple nodes able to perform the same task, enables the system to scale with demand. Furthermore, physical workflow nodes (operator instances) are able to locate and select the next filter or fusion operator instance autonomously, while ensuring the correct execution of the workflow.
A.J. Chakravarti, Gerald Baumgartner, Mario Lauria
The Organic Grid is a biologically inspired and fully decentralized approach to the organization of computation that is based on the autonomous scheduling of strongly mobile agents on a peer-to-peer network. Through the careful design of agent behavior, the emerging organization of the computation can be customized for different classes of applications. In this paper, we report on our experience in adapting the general framework to run two representative applications on our Organic Grid prototype: the National Center for Biotechnology Information (NCBI) basic local alignment search tool (BLAST) code for sequence alignment, and the Cannon's algorithm for matrix multiplication. The first is an example of independent task application, a type of application commonly used for grid scheduling research because of its easily decomposable nature and absence of intra-node communication. The second is a popular block algorithm for parallel matrix multiplication, and represents a challenging application for grid platforms because of its highly structured and synchronous communication pattern. Agent behavior completely determines the way computation is organized on the Organic Grid. We intentionally chose two applications at opposite ends of the distributed computing spectrum having very different requirements in terms of communication topology, resource use, and response to faults. We detail the design of the agent behavior and show how the different requirements can be satisfied. By encapsulating application code and scheduling functionality into mobile agents, we decouple both computation and scheduling from the underlying grid infrastructure. In the resulting system, every node can inject a computation onto the grid; the computation naturally organizes itself around available resources.
Grid computing is an open, heterogeneous and highly dynamic environment based on the principles of service oriented computing. It focuses on basic infrastructure for coordinated resource sharing among virtual organizations to achieve high performance and availability. However, use of existing Grid computing environment is quite complex and requires a lot of human intervention. In order to avoid this intervention, enhancements are required in bringing autonomy and semantics in existing Grid infrastructure. Semantics would act as glue for autonomy in the process of efficient resource discovery and utilization. Several ontologies and ontology languages have been proposed in this regard which not only have some shortcoming but also poses a sort of overhead for the Grid environment. On the other hand, agents are autonomous problem solving entities, and can negotiate semantically for interoperation with each other in dynamic environments. Inspired from the concept of Autonomous Decentralized Systems, we propose that the above mentioned goals can be achieved by integrating FIPA Multi Agent Systems with the Grid Service Architecture and hence to lay the foundation for Autonomous Semantic Grid. Autonomous Semantic Grid system architecture is aimed to provide an improved infrastructure by bringing autonomy, semantic interoperability and decentralization in the Grid computing for emerging applications. This paper then presents implementation details of first milestone toward Autonomous Semantic Grid realization based on a middleware, namely AgentWeb Gateway for integration of Multi Agent Systems and Grid Service Architecture. Evaluation of the system has also been performed over a number of application scenarios.