Resource allocation for query processing in grid systems: A survey

Grid systems are very useful platforms for distributed databases, especially in some situations in which the scale of data sources and user requests is very high. However, the main characteristics of grid systems such as dynamicity, large size and heterogeneity, bring new problems to the query processing domain such as resource discovery and resource allocation. In this paper, we provide a survey related to resource allocation methods for query processing In data grid systems. We provide a classification for existing studies considering their approaches to the resource allocation problem. We provide a synthesis of the studies and propose evaluations and comparisons for the different classes of studies. ©2012 CRL Publishing Ltd

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Selecting Metrics to Evaluate Human Supervisory Control Applications

The goal of this research is to develop a methodology to select supervisory control metrics. This methodology is based on cost-benefit analyses and generic metric classes. In the context of this research, a metric class is defined as the set of metrics that quantify a certain aspect or component of a system. Generic metric classes are developed because metrics are mission-specific, but metric classes are generalizable across different missions. Cost-benefit analyses are utilized because each metric set has advantages, limitations, and costs, thus the added value of different sets for a given context can be calculated to select the set that maximizes value and minimizes costs. This report summarizes the findings of the first part of this research effort that has focused on developing a supervisory control metric taxonomy that defines generic metric classes and categorizes existing metrics. Future research will focus on applying cost benefit analysis methodologies to metric selection. Five main metric classes have been identified that apply to supervisory control teams composed of humans and autonomous platforms: mission effectiveness, autonomous platform behavior efficiency, human behavior efficiency, human behavior precursors, and collaborative metrics. Mission effectiveness measures how well the mission goals are achieved. Autonomous platform and human behavior efficiency measure the actions and decisions made by the humans and the automation that compose the team. Human behavior precursors measure human initial state, including certain attitudes and cognitive constructs that can be the cause of and drive a given behavior. Collaborative metrics address three different aspects of collaboration: collaboration between the human and the autonomous platform he is controlling, collaboration among humans that compose the team, and autonomous collaboration among platforms. These five metric classes have been populated with metrics and measuring techniques from the existing literature. Which specific metrics should be used to evaluate a system will depend on many factors, but as a rule-of-thumb, we propose that at a minimum, one metric from each class should be used to provide a multi-dimensional assessment of the human-automation team. To determine what the impact on our research has been by not following such a principled approach, we evaluated recent large-scale supervisory control experiments conducted in the MIT Humans and Automation Laboratory. The results show that prior to adapting this metric classification approach, we were fairly consistent in measuring mission effectiveness and human behavior through such metrics as reaction times and decision accuracies. However, despite our supervisory control focus, we were remiss in gathering attention allocation metrics and collaboration metrics, and we often gathered too many correlated metrics that were redundant and wasteful. This meta-analysis of our experimental shortcomings reflect those in the general research population in that we tended to gravitate to popular metrics that are relatively easy to gather, without a clear understanding of exactly what aspect of the systems we were measuring and how the various metrics informed an overall research question

MBA: A market-based approach to data allocation and migration for cloud database

With the coming shift to cloud computing, cloud database is emerging to provide database service over the Internet. In the cloud-based environment, data are distributed at internet scale and the system needs to handle a huge number of user queries simultaneously without delay. How data are distributed among the servers has a crucial impact on the query load distribution and the system response time. In this paper, we propose a market-based control method, called MBA, to achieve query load balance via reasonable data distribution. In MBA, database nodes are treated as traders in a market, and certain market rules are used to intelligently decide data allocation and migration. We built a prototype system and conducted extensive experiments. Experimental results show that the MBA method signicantly improves system performance in terms of average query response time and fairness

The Role of Human-Automation Consensus in Multiple Unmanned Vehicle Scheduling

Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation’s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.Aurora Flight Sciences Corp.United States. Office of Naval Researc