Cloud Computing is becoming more and more a commodity service to all kinds of businesses. This leads to a stronger need for dependable service guarantees on the resources or applications offered to the customer by the provider via Service Level Agreements. Most offerings on the market, however, rely on non-electronic modes rather than machine-manageable means. In this paper, we propose a protocol that closes this gap for REST-based services and leverages the benefits of HTTP. We build upon the widely established WS-Agreement document format and show how our protocol integrates with the currently popular Open Cloud Computing Interface.

In this paper, we address the problem of finding well-performing workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a noninvasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and which leaves the local resource managers completely untouched. In this environment of fully autonomous sites, independent users are assumed to submit their jobs to the Grid middleware layer of their local site, which in turn decides on the delegation and execution either on the local system or on remote sites in a situation-dependent, adaptive way. We find for different scenarios that the exchange policies show good performance characteristics not only with respect to traditional metrics such as average weighted response time and utilization, but also in terms of robustness and stability in changing environments.

In our work, we utilize a competitive Co-evolutionary Algorithm in order to optimize the parameter set of a Fuzzy System for job exchange in Computational Grids. In this domain, the providers of High Performance Computing (HPC) centers strive for minimizing the response time for their own customers by trying to distribute workload to other sites in the Grid environment. The Fuzzy System is used for steering each site's decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. This scenario is particularly suited for the application of a competitive CA: Grid sites' Fuzzy Systems are modeled as species, which evolve in different populations. While each species tries to minimize the response time for locally submitted jobs, their individuals' fitness is determined within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each Grid site and the overall system.

In our work, we address the problem of workload distribution within a computational grid. In this scenario, users submit jobs to local high performance computing (HPC) systems which are, in turn, interconnected such that the exchange of jobs to other sites becomes possible. Providers are able to avoid local execution of jobs by offering them to other HPC sites. In our implementation, this distribution decision is made by a fuzzy system controller whose parameters can be adjusted to establish different exchange behaviors. In such a system, it is essential that HPC sites can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. However, each site egoistically strives only for the minimization of its own jobs' response times regularly at the expense of other sites. This scenario is particularly suited for the application of a competitive coevolutionary algorithm: the fuzzy systems of the participating HPC sites are modeled as species that evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and grid setups, we show that opportunistic cooperation leads to significant improvements for each HPC site as well as for the overall system.

This paper examines the job exchange between parallel compute sites in a decentralized grid scenario. Here, the local scheduling system remains untouched and continues normal operation. In order to establish the collaboration and interaction between sites in a grid context, a middleware layer that is responsible for the migration of jobs is supplemented. Independent users are assumed to submit their jobs to their site-local middleware layer, which in turn can request jobs for execution from alien sites. The simulation results are obtained using real workload traces and compared to the performance of the EASY Backfilling algorithm in an equal single-site scenario. It is shown that collaboration between site is beneficial for all high utilized participants as it is possible to achieve shorter response times for jobs compared to the best single-site scheduling results.

In this paper, we introduce a methodology for the approximation of optimal solutions for a resource allocation problem in the domain of Grid scheduling on High Performance Computing systems. In detail, we review a real-world scenario with decentralized, equitable, and autonomously acting suppliers of compute power who wish to collaborate in the provision of their resources. We exemplarily apply NSGA-II in order to explore the bounds of maximum achievable benefit. To this end, appropriate encoding schemes and variation operators are developed while the performance is evaluated. The simulations are based upon recordings from real-world Massively Parallel Processing systems that span a period of eleven months and comprise approximately 100,000 jobs. By means of the obtained Pareto front we are able to identify bounds for the maximum benefit of Grid computing in a popular scenario. For the first time, this enables Grid scheduling researchers to rank their developed real-world strategies.

In this paper, we utilize a predator-prey model in order to identify characteristics of single-objective variation operators in the multi-objective problem domain. In detail, we analyze exemplarily Gaussian mutation and simplex recombination to find explanations for the observed behaviorswithin this model. Then, both operators are combinedto a new complex one for the multi-objective case in order to aggregate the identified properties. Finally, we show that (a) characteristic properties can still be observed in the combination and (b) the collaboration of those operators is beneficial for solving an exemplary multi-objective problem regarding convergence and diversity.