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With the advancement of networking technologies and large-scale social computing, multi-user collaboration is becoming popular. In large-scale and dynamic collaborative environments, users may arbitrarily join or quit a collaboration group and work on common tasks for a long time. When users synchronize their work with the other collaborators, a large number of concurrent updates from the collaborators must be reconciled or merged to produce a consistent document state, which could challenge existing consistency maintenance approaches. Our idea is to accelerate the commutative replicated data type (CRDT)-based consistency maintenance algorithms with multi-core processors. We theoretically prove a general commutative condition for CRDT operations. One typical CRDT algorithm, replicated growable array (RGA), is transformed into Parallel RGA (PRGA) under the guidance of the general commutative condition. PRGA allows a batch of RGA operations to be executed in parallel. Experimental results showed that when processing millions of concurrent updates, PRGA achieves approximately 4 × performance gains over RGA on an ordinary four-core processor. [ABSTRACT FROM AUTHOR]

Efficiently implementing the divide-and-conquer pattern of parallelism in distributed memory systems is very relevant, given its ubiquity, and difficult, given its recursive nature and the need to exchange tasks and data among the processors. This task is noticeably further complicated in the presence of multi-core systems, where hybrid parallelism must be exploited to attain the best performance, and when unbalanced and deep workloads are considered, as additional measures must be taken to load balance and avoid deep recursion problems. In this manuscript a parallel skeleton that fulfills all these requirements while providing high levels of usability is presented. In fact, the evaluation shows that our proposal is on average 415.32% faster than MPI codes and 229.18% faster than MPI + OpenMP benchmarks, while offering an average improvement in the programmability metrics of 131.04% over MPI alternatives and 155.18% over MPI + OpenMP solutions. [ABSTRACT FROM AUTHOR]

Recent advancements of virtualization technologies for parallel processing involve scheduling containerized tasks in a workflow. Since a container can include multiple tasks, it can be reused or shared among applications. If every task in a workflow uses its dedicated container without sharing among any tasks, each container image must be downloaded for each task. As a result, many computational resources are required to process and the communication latency related to container image downloading can become a bottleneck for the makespan. In task scheduling algorithms for workflows, this characteristic produces a new challenging issue that how effectively shares containers among tasks to avoid redundant container image download processes and redundant task allocations. One of the fundamental problems is that no policy has been established for simultaneously satisfying effective container sharing, maintaining the degree of task parallelism, and effective computational resource utilization. In this paper, we propose a clustering-based containerized task scheduling algorithm for clouds, namely, shareable functional task clustering for utilizing virtualized resources (SF-CUV). The objective of SF-CUV is to minimize the makespan with less computational resources and containers than other algorithms by clustering tasks and sharing each container among tasks. SF-CUV consists of two phases: (i)task clustering and pre-virtual CPU (vCPU) allocation phase to derive an accurate scheduling priority, and (ii)task ordering and actual task reallocation phase. Experimental results obtained via simulation and in a real environment show that SF-CUV can utilize both vCPUs and containers with a shorter makespan compared with other approaches. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved. [ABSTRACT FROM AUTHOR]

Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise with the help of scheduling algorithms to their respective processors. For task assignment, every scheduling algorithm follows different protocols like upper bound of CPU utilization, assigning priorities, etc. In this paper, author has worked on such scheduling algorithms. Previously, the author evaluated the performance of algorithms on the basis of transactions (group of tasks). In this paper, the author re-evaluates joint EDF–RM scheduling algorithm, where its performance is calculated on the execution of individual task basis. For comparative analysis, similar algorithms are considered, i.e., joint EDF–RMS, earliest deadline first (EDF) and rate monotonic scheduling (RMS). These mentioned algorithms are simulated and analyzed with the help of statistical analysis, and turnaround time of periodic tasks is evaluated. Additionally, migration distribution and CPU utilization on the basis of scheduling algorithms' upper bounds are also calculated. [ABSTRACT FROM AUTHOR]

In recent years, data centers have deployed many rapidly growing Internet-scale services. As the size of the data center continues to expand, it not only causes huge energy consumption, but also brings environmental problems such as huge carbon emissions. However, for an Internet-scale data center, it is a challenge to optimize power consumption while meeting the growing demands of Internet-scale applications. Moreover, current data centers usually have heterogeneous servers (with different power consumption and computing capacity), which causes the dynamic task placement problem more complicated. In this work, we first observe two kinds of heterogeneity from the analysis of a public trace that was collected from a Google data center. Considering the demands in energy consumption and performance, we model the dynamic task placement problem in a heterogeneous Internet-scale data center and propose a heuristic cost-aware algorithm to solve it. By simulating and comparing with the other two scheduling algorithms, our proposed heuristic algorithm can gain a well energy saving and keep application performance within acceptable limits. [ABSTRACT FROM AUTHOR]