A Novel Multitask Scheduling and Distributed Collaborative Computing Method of Edge Nodes in the Internet of Things

In this paper, we propose a novel multitask scheduling and distributed collaborative computing method for quality of service (QoS) guaranteed delay-sensitive services in the Internet of Things (IoT). First, we propose a multilevel scheduling framework combining the process and thread scheduling for reducing the processing delay of multitype services of a single edge node in IoT, where a preemptive static priority process scheduling algorithm is adopted for different types of services and a dynamic priority-based thread scheduling algorithm is proposed for the same type of services with high concurrency. Furthermore, for reducing the processing delay of computation-intensive services, we propose a distributed task offloading algorithm based on a multiple 0-1 knapsack model with value limitation with the collaboration of multiple edge nodes to minimize the processing delay. Simulation results show that the proposed method can significantly reduce not only the scheduling delay of a large number of time-sensitive services in single edge node but also the process delay of computation-intensive service collaborated by multiple edge nodes.

PAVER

The massive parallelism present in GPUs comes at the cost of reduced L1 and L2 cache sizes per thread, leading to serious cache contention problems such as thrashing. Hence, the data access locality of an application should be considered during thread scheduling to improve execution time and energy consumption. Recent works have tried to use the locality behavior of regular and structured applications in thread scheduling, but the difficult case of irregular and unstructured parallel applications remains to be explored. We present PAVER , a P riority- A ware V ertex schedul ER , which takes a graph-theoretic approach toward thread scheduling. We analyze the cache locality behavior among thread blocks ( TBs ) through a just-in-time compilation, and represent the problem using a graph representing the TBs and the locality among them. This graph is then partitioned to TB groups that display maximum data sharing, which are then assigned to the same streaming multiprocessor by the locality-aware TB scheduler. Through exhaustive simulation in Fermi, Pascal, and Volta architectures using a number of scheduling techniques, we show that PAVER reduces L2 accesses by 43.3%, 48.5%, and 40.21% and increases the average performance benefit by 29%, 49.1%, and 41.2% for the benchmarks with high inter-TB locality.

Cluster Of Workstations for cloud foundry based functions and applications

In this publication we describe on OpenRAN based cluster of workstations, implementing cloud foundry Kubernetes, architecture for AWS inspired Lambdas and threads on cloud foundry, implemented on dedicated units as a cluster and on robotic segway RMP 440 platforms as HTCondor based thread schedulers, using PyMACS integrated with a resident kernel inspired by the shadow process of HT Condor. This allows for thread scheduling on idle robotic CPUsKubernetes, Cloud Foundry, AWS, VPN, HTCondor.

A Layered Mapping of Ada 202X to OpenMP

The OpenMP specification defines a set of compiler directives, library routines, and environment variables that together represent the OpenMP Application Programming Interface, and is currently defined for C, C++, and Fortran. The forthcoming version of Ada, currently dubbed Ada 202X, includes lightweight parallelism features, in particular parallel blocks and parallel loops. All versions of Ada, since its inception in 1983, have included "tasking," which corresponds to what are traditionally considered "heavyweight" parallelism features, or simply "concurrency" features. Ada "tasks" typically map to what are called "kernel threads," in that the operating system manages them and schedules them. However, one of the goals of lightweight parallelism is to reduce overhead by doing more of the management outside the kernel of the operating system, using a light-weight-thread (LWT) scheduler. The OpenMP library routines support both levels of threading, but for Ada 202X, the main interest is in making use of OpenMP for its lightweight thread scheduling capabilities.

Plagiarism Detection of Multi-threaded Programs Using Frequent Behavioral Pattern Mining

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage the Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program’s behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multi-threaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth copes better with multi-threaded plagiarism detection than alternative approaches. Compared against the dynamic birthmark System Call Short Sequence Birthmark (SCSSB), FPBirth achieves 12.4%, 4.1% and 7.9% performance improvements with respect to union of resilience and credibility (URC), F-Measure and matthews correlation coefficient (MCC) metric, respectively.