Expected Task Completion Time Research Articles

Co-residence based data theft game in cloud system with virtual machine replication and cancellation

In cloud computing, the task replication with cancellation (TRC) approach aims to reduce the expected task completion time while mitigating additional load and user expenses. However, its effectiveness may be hindered by co-residence attacks, where attackers may access users’ data through co-residing their virtual machines (VMs) with users’ VMs on the same server. Particularly, creating more task replicas has two-fold effects: a task has larger chances to complete in a shorter time but becomes more vulnerable to co-residence attacks (incurring higher data theft probability). The objective of this paper is to formulate and solve an optimization problem (a minmax game problem) that finds the optimal number of task replicas balancing the two conflicting effects. Resulting solutions to the proposed optimization problem can maximize the user's utility while considering the strategic attack behavior of maximizing the attacker's utility. The solution methodology encompasses a new probabilistic model of evaluating the user's and attacker's utilities. As demonstrated through examples, there exist complicated interactions of different model parameters impacting the utilities of users and attackers and thus the optimization solutions, justifying necessity and significance of the suggested model and methodology.

Minimization of Expected User Losses Considering Co-resident Attacks in Cloud System with Task Replication and Cancellation

Advances in cyber-physical systems have engendered strong needs to use cloud computing for data storage and task processing. This paper models a cloud system implementing the task replication with cancellation (TRC) technique to improve the successful completion probability of a real-time task while mitigating additional system loads and user expenses. Particularly, a task and its replicas are processed concurrently by different virtual machines (VMs); the successful completion of any task copy within the deadline triggers a cancellation of all the replicas. More replicas can improve effectiveness of the TRC approach, which however makes the task more vulnerable to co-resident attacks, where a malicious attacker may steal users’ data through co-residing its VMs on the same physical server as users’ VMs. This work solves optimization problems that determine the optimal number of task replicas to minimize the expected user losses, achieving a balance between the task completion probability and the data theft success probability. The solution methodology encompasses a probabilistic model for evaluating the task completion probability by a certain deadline, expected task completion time, and data theft success probability. Examples are presented to demonstrate effects of different parameters (the number of cloud servers, the number of attacker's VMs, and the task deadline) on task performance metrics and optimization solutions.

HANDLING PERCEPTION UNCERTAINTY IN SIMULATION BASED SINGULATION PLANNING FOR ROBOTIC BIN PICKING.

Robotic bin picking requires using a perception system to estimate the pose of the parts in the bin. The selected singulation plan should be robust with respect to perception uncertainties. If the estimated posture is significantly different from the actual posture, then the singulation plan may fail during execution. In such cases, the singulation process will need to be repeated. We are interested in selecting singulation plans that minimize the expected task completion time. In order to estimate the expected task completion time for a proposed singulation plan, we need to estimate the probability of success and the plan execution time. Robotic bin picking needs to be done in real-time. Therefore, candidate singulation plans need to be generated and evaluated in real-time. This paper presents an approach for utilizing computationally efficient simulations for generating singulation plans. Results from physical experiments match well with predictions obtained from simulations.

On the performance and power consumption analysis of elastic clouds

SummaryCloud computing is a novel paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Elasticity is one of the most attractive features of cloud computing. Elastic clouds are able to adapt to workload changes by provisioning and de‐provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible. However, elasticity adds complexity, which makes quantitative analysis of cloud performance and power consumption difficult. Such analysis is required to evaluate and quantify the cost‐benefit of a strategy portfolio and the quantitative runtime performance and power consumption experienced by cloud‐users. In this study, we present a comprehensive analytical approach to performance and power consumption analysis of elastic clouds. Several metrics are defined and evaluated: expected task completion time, power consumption rate, and task rejection rate under different load conditions, elasticity intensities, and error intensities. To validate the proposed approach, we obtain experimental data through a real‐world cloud and conduct a confidence interval analysis. The analysis results suggest the perfect coverage of theoretical results by corresponding experimental confidence intervals. Copyright © 2016 John Wiley & Sons, Ltd.

A probabilistic model for performance analysis of cloud infrastructures

SummaryAnalyzing the quantitative performance plays an important role in understanding and improving the quality of cloud computing systems and cloud‐based applications. In cloud computing, service requests from users go through numerous provider‐specific steps from the instant it is submitted to when the requested service is fully delivered. Quantitative performance analysis is not an easy task because of the complexity of cloud provisioning control flows and the increasing scale and complexity of real‐world cloud infrastructures. This work proposes a probabilistic queuing network‐based model for the performance analysis of cloud infrastructures. It considers expected task completion time and rejection probability as the performance metrics. Experimental performance data suggest the correctness of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd.

Stochastic Modeling and Performance Analysis of Migration-Enabled and Error-Prone Clouds

Cloud computing is a promising paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Virtualization allows to instantiate virtual machines (VMs) on top of fewer physical systems managed by a VM manager. Performance evaluation of clouds is required to evaluate and quantify the cost-benefit of a strategy portfolio and the quality of service (QoS) experienced by end-users. Such evaluation is not feasible by means of simulation or on-the-field measurement, due to the great scale of parameter spaces that have to be traversed. In this study, we present a stochastic-queuing-network-based approach to performance analysis of migration-enabled clouds in error-prone environment. Several performance metrics are defined and evaluated: utilization, expected task completion time, and task rejection rate under different load conditions and error intensities. To validate the proposed approach, we obtain experimental performance data through a real-world cloud and conduct a confidence-interval analysis. The analysis results suggest the perfect coverage of theoretical performance results by corresponding experimental confidence intervals.