Project Scheduling is important since it plays an effective role in project success. To organize and complete your projects in a timely, quality and financially responsible manner, you need to schedule projects carefully. Effective project scheduling plays a crucial role in ensuring project success.
Scheduling is used to keep projects on track, s...
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Cloud computing offers on-demand availability of computing resources over the Internet. To attract users, cloud providers offer their resources as services at reasonable prices and provide various price models to reflect higher level of quality of service (QoS), which are referred as pricing schemes. k-times anonymous authentication (k-TAA) is an a...
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Many retail stores, as well as other organizations that employ a multitude of part-time employees, rely on developing schedules frequently, since the availabilities of the employees as well as the needs of the business change often. This process is often performed on a weekly basis, and is complex and time-consuming. The schedule must typically sat...
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In such systems, the efficiency of task scheduling algorithms directly impacts the overall system performance. By using previously proposed 2D scheduling model, existing algorithms could not provide an efficient way to find all suitable allocations. In addition, most of them ignored the single reconfiguration port constraint and inter-task dependen...
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Deep neural network (DNN) has become increasingly popular in industrial IoT scenarios. Due to high demands on computational capability, it is hard for DNN-based applications to directly run on intelligent end devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks t...
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The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networ...
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Today's IoT devices rely on batteries, which offer stable energy storage but contain harmful chemicals. Having billions of IoT devices powered by batteries is not sustainable for the future. As an alternative, batteryless devices run on long-lived capacitors charged using energy harvesters. The small energy storage capacity of capacitors results in...
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We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT servi...
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Cloud computing is becoming an popular model of computing. Due to the increasing complexity of the cloud service requests, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, ...
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Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known as cloudlets,...
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Due to the excessive concentration of computing resources in the traditional centralized cloud service system, there will be three prominent problems of management confusion, construction cost and network delay. Therefore, we propose to virtualize regional edge computing resources in intelligent buildings as edge service pooling, then presents a hi...
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In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challen...
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The rapid development of the Industrial Internet of Things (IIoT) has led to the explosive growth of industrial control data. Cloud computing-based industrial control models cause vast energy consumption. Most existing solutions try to reduce the overall energy consumption by optimizing task scheduling and disregard how to reduce the load of comput...
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The reliance on Network-on-Chip (NoC)-based Multiprocessor Systems-on-Chips (MPSoCs) is proliferating in modern embedded systems to satisfy the higher performance requirement of multimedia streaming applications. Task level coarse grained software pipeling also called re-timing when combined with Dynamic Voltage and Frequency Scaling (DVFS) has sho...
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We consider a distributed IoT edge network whose end nodes generate computation jobs that can be processed locally or be offloaded, in full or in part, to other IoT nodes and/or edge servers having the necessary computation and energy resources. That is, jobs can either be partitioned and executed at multiple nodes (including the originating no...
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Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various computation spots, each with very limited c...
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Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This p...
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The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research...
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Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. Scheduling workflows turns to the main bottleneck for increasing resource utiliza...
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Nowadays, more and more computation-intensive scientific applications with diverse needs are migrating to cloud computing systems. However, the cloud systems alone cannot meet applications' requirements at all times with the increasing demands from users. Therefore, the multi-cloud systems that can provide scalable storage and computing resources b...
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The elasticity of cloud resources allow cloud clients to expand and shrink their demand of resources dynamically over time. However, fluctuations in the resource demands and pre-defined size of virtual machines (VMs) lead to lack of resource utilization, load imbalance and excessive power consumption. To address these issues and to improve the perf...
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The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such environments is challenging due to constrained resource capabilities, mobility factors in IoT, ...
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Fog computing as an extension of the cloud based infrastructure, provides a better computing platform than cloud computing for mobile computing, Internet of Things, etc. One of the problems is how to make full use of the resources of the fog so that more requests of applications can be executed on the edge, reducing the pressure on the network and ...
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Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this paper, ...
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