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To deal with the problem of unbalanced load and ignoring task priority in previous task scheduling algorithms, this paper proposes a task scheduling algorithm named P-Min-Min-Max, which combines with priority and greedy strategy. Under the condition of considering the task priority, the algorithm leverages the execution time of tasks for greedy strategy, and binding large task and small task in the task list to form "task pair" to perform scheduling, so as to effectively solve the problem of unbalanced load. To reduce the average response time of tasks, the algorithm priority schedules the small task from the "task pair". The experimental results show that, compared with the Min-Min and P-Min-Min algorithm, the proposed algorithm improves the system resource utilization and service quality of user, and saves the total execution time for tasks. Compared with Max-Min and P-Max-Min algorithm, the proposed algorithm improves the system resource utilization and service quality of user, and reduces the total completion time and average response time. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has gained more and more attention from industrial and academic circle since it offers pay‐as‐you‐go model, and business applications based on the cloud are also increasing. These applications meet the requirement of users while at the same time triggering the problem of high energy consumption in data centers. To deal with the problem, we propose a new algorithm named EEOM (Energy Efficiency Optimization of VM Migrations). Under considering CPU and memory factors, the key three steps for EEOM algorithm, including trigger time, VM selection, and host location, are optimized. EEOM algorithm takes use of the virtualization technology and migrates some VMs on the lightly loaded host and heavily loaded host to other hosts. The idle hosts are switched to low‐power mode or shut down so as to save energy consumption. The experimental results show that, as compared with Double Threshold (DT) algorithm, the EEOM algorithm saves 7% energy consumption and reduces 13% SLA violations. [ABSTRACT FROM AUTHOR]

In abundant location-based service applications, it is necessary to process continuous spatial keyword queries over geo-textual data streaming. As an important spatial keyword query, the collective spatial keyword (CSK) query aims to find a set of objects such that it covers all the given keywords collectively, the objects within the set are nearest to the query point, and it has the minimum distance between different objects. The existing approaches for the CSK query are mostly index-based algorithms. Although these approaches gain superior performance, their applicability is significantly limited by the necessity to create an index to organize the dataset. Therefore, these index-based approaches cannot be utilized to process data streaming that prevalently exists in most location-based service applications. In addition, the existing algorithms have much room for improvement as the distances between different objects are overlooked when generating feasible candidate sets. Moreover, the results returned by the proposed algorithms could be further refined to offer better decision support for users. In this paper, a greedy algorithm and an approximate algorithm with a provable approximate bound are proposed for the CSK query. Our approaches are appropriate to the CSK queries where the datasets are not suitable to be organized by indexes and can get better query results with less objects and smaller function scores. To boost the query performance, new pruning strategies and heuristic rules are developed. The experimental results demonstrate scalability, efficiency, and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]