Search

As high‐performance computing (HPC) becomes a tool used in many different workflows, quality of service (QoS) becomes increasingly important. In many cases, this includes the reliable execution of an HPC job and the generation of the results by a certain deadline. The resource and job management system (RJMS) or simply RMS is responsible for receiving the job requests and executing the jobs with a deadline‐oriented policy to support the workflows. In this article, we evaluate how well static resource management policies cope with deadline‐constrained HPC jobs and explore two variations of a dynamic policy in this context. As the Hilbert curve‐based approach used by the SLURM workload manager represents the state‐of‐the‐art in production environments, it was selected as one of the static allocation strategies. The Manhattan median approach as a second allocation strategy was introduced as a research work that aims to minimize the communication overhead of the parallel programs by providing compact partitions more than the Hilbert curve approach. In contrast to the static partitions provided by the Hilbert curve approach and the Manhattan median approach, the leak approach focuses on supporting dynamic runtime behavior of the jobs and assigning nodes of the HPC system on demand at runtime. Since the contiguous leak version also relies on a compact set of nodes, the noncontiguous leak can provide additional nodes at a greater distance from the nodes already used by the job. Our preliminary results clearly show that a dynamic policy is needed to meet the requirements of a modern deadline‐oriented RMS scenario. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Scientific Publishing is the property of Research & Development of Human Recourses Center (REMAH) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ensuring relative consistency in executing temporal queries to access real-time sensor data streams maintained in a database is a challenging problem, particularly when data transmission delays are lengthy and highly variable. Due to the unordered arrivals of sensor data, the databases may contain numerous open data versions (ODVs) with undefined validity intervals. Accessing ODVs may violate the relative consistency requirements of temporal queries, resulting in incorrect results. Although the Re-execution with Every Update (REU) method can resolve this issue, it may introduce heavy re-execution costs and significant delays in query completion. In this article, we study the problem of retrieving data items with temporal consistency requirements in a multi-data-stream database. To balance response time and meet the relative consistency requirements of queries, we introduce an enhanced REU mechanism called Re-Execution with Deadline (RED). Moreover, we propose a novel optional mechanism called Backward Execution Option (BEO) for temporal queries to achieve relative consistency in their execution with quick results by relaxing the data freshness constraints. By combining RED with BEO, we formulate the Repeated BEO (RBEO) to further reduce the query response time. We extend the timestamped key-value store MVLevelDB into MVLevelDB+ to implement the proposed mechanisms. To reduce the query re-execution cost as required in RED and REU, we designed the Query Pool with Execution State (QpES) mechanism to achieve relative consistency in query execution with lower checking overhead and only one re-execution. We conducted extensive evaluation experiments on MVLevelDB+ using benchmark programs to illustrate their performance characteristics on handling temporal queries in the modeled IoT system. [ABSTRACT FROM AUTHOR]

Time-sensitive networking (TSN) is an emerging technology widely used in real-time systems for its high bandwidth and deterministic timing properties. To ensure the deterministic transmission of Time-triggered (TT) messages, a guard band mechanism is employed to prevent interference from other messages, such as Audio-Video Bridging (AVB) and Best-effort (BE) messages, before transmitting the TT messages in TSN. However, this mechanism introduces transmission delays for non-TT messages and bandwidth wastes for the physical links. Another challenge arises from the default First-in-first-out (FIFO) order of incoming messages, resulting in a relatively low acceptance rate for non-TT messages. To address these issues, a hybrid scheduling algorithm based on the min-heap structure (HSMH) is proposed. For AVB messages, HSMH sorts them in ascending style on the basis of deadlines, guaranteeing the earliest deadline message to be sent first. For BE messages, a threshold is designed to diverge them into two queues: a FIFO queue and a STF (shortest-time-first) queue. The former outputs the messages in a FIFO style, while the latter outputs messages in a STF style. All the output order of AVB messages and STF-queue messages are arranged in a min-heap structure. The algorithm can efficiently improve the transmission rate of AVB messages, the sending rate of BE messages, and the overall link utilization. Experimental results demonstrate that the proposed algorithm outperforms existing approaches in all these three aspects. [ABSTRACT FROM AUTHOR]

Copyright of Natur und Recht is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract‘Leaving No-One Behind’ (LNOB) is the guiding principle of the Sustainable Development Goals (SDGs), aiming to include the most marginalized populations in development progress. However, the growing digitalization of development presents challenges for LNOB, as vulnerable groups risk being excluded when development goes digital. Addressing digital inequalities (DI) is therefore essential for fostering inclusivity, yet the current SDGs provide little to no guidance on this issue. As the SDGs approach their 2030 deadline, it is critical to consider how a future framework could better address DI. This article examines how DI-sensitive development goals could promote equitable digital development. Based on interviews with digital development professionals from various backgrounds, we identify three key purposes DI-sensitive goals could serve: (1) putting DI on the agenda, (2) normalizing DI-sensitive approaches, and (3) mobilizing actors and resources. The analysis explores how different integration strategies – a standalone goal, cross-cutting indicators, or a combination of both – could support these purposes. By outlining the implications of each approach, the article provides a foundation for shaping the next SDGs to address DI and foster inclusive development in a digital world. [ABSTRACT FROM AUTHOR]

Timing determinism in embedded real-time systems requires meeting timing constraints not only for individual tasks but also for chains of tasks that involve multiple messages. End-to-end analysis is a commonly used approach for solving such problems. However, the temporal properties of tasks often have uncertainty, which makes end-to-end analysis challenging and prone to errors. In this paper, we focus on enhancing the precision and safety of end-to-end timing analysis by introducing a novel probabilistic method. Our approach involves establishing a probabilistic model for end-to-end timing analysis and implementing two algorithms: the maximum data age detection algorithm and the end-to-end timing deadline miss probability detection algorithm. The experimental results indicate that our approach surpasses traditional analytical methods in terms of safety and significantly enhances the capability to detect the probability of missing end-to-end deadlines. [ABSTRACT FROM AUTHOR]

We examine the underlying cognitive mechanisms that govern how competitions play out over time. We used cognitive modeling to examine the dynamic effects of time remaining and relative performance (whether the person is winning or losing) on effort and strategy. In this experiment, participants completed a competitive decision-making task with varying time limits and starting scores, in a repeated-measures design. Participants were tasked with scoring more points than their computerized opponent during a certain time frame, gaining and losing points for correct and incorrect decisions, respectively. The results showed that as the competition deadline approached and as participants drew ahead of their opponent within a competition, they increased effort and became more cautious. Furthermore, the effect of relative score on effort and caution changed over the course of a competition as the deadline approached. These results highlight the importance of considering dynamics when working to understand how competitions unfold as well as the underlying cognitive mechanisms that give rise to the dynamic behavior. [ABSTRACT FROM AUTHOR]

Data centers are a vital and fundamental infrastructure component of the cloud. The requirement to execute a large number of demanding jobs places a premium on processing capacity. Parallelizing jobs to run on multiple cores reduces execution time. However, there is a decreasing marginal benefit to using more cores, with the speedup function quantifying the achievable gains. A critical performance metric is flow time. Previous results in the literature derived closed-form expressions for the optimal allocation of cores to minimize total flow time for a power-law speedup function if all jobs are present at time 0. However, this work did not place a constraint on the makespan. For many diverse applications, fast response times are essential, and latency targets are specified to avoid adverse impacts on user experience. This paper is the first to determine the optimal core allocations for a multicore system to minimize total flow time in the presence of a completion deadline (where all jobs have the same deadline). The allocation problem is formulated as a nonlinear optimization program that is solved using the Lagrange multiplier technique. Closed-form expressions are derived for the optimal core allocations, total flow time, and makespan, which can be fitted to a specified deadline by adjusting the value of a single Lagrange multiplier. Compared to the unconstrained problem, the shortest job first property for optimal allocation is maintained; however, a number of other properties require revising and other properties are only retained in a modified form (such as the scale-free and size-dependence properties). It is found that with a completion deadline the optimal solution may contain groups of simultaneous completions. In general, all possible patterns of single- and group-completion need to be considered, producing an exponential search space. However, the paper determines analytically that the optimal completion pattern consists of a sequence of single completions followed by a single group of simultaneous completions at the end, which reduces the search space dimension to being linear. The paper validates the Lagrange multiplier approach by verifying constraint qualifications. [ABSTRACT FROM AUTHOR]

This article discusses how adapting assessment and grading practices based on equity principles may impact students positively, considering their specific needs in the post pandemic era. We reflect on how some equitable grading practices such as flexible deadlines, a friendly tone of communication, and allowing multiple attempts have a positive impact on students' personal and academic life. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on a resource leveling variant of a two-processor scheduling problem. The latter problem is to schedule a set of dependent UET jobs on two identical processors with minimum makespan. It is known to be polynomial-time solvable. In the variant we consider, the resource constraint on processors is relaxed and the objective is no longer to minimize makespan. Instead, a deadline is imposed on the makespan and the objective is to minimize the total resource use exceeding a threshold resource level of two. This resource leveling criterion is known as the total overload cost. Sophisticated matching arguments allow us to provide a polynomial algorithm computing the optimal solution as a function of the makespan deadline. It extends a solving method from the literature for the two-processor scheduling problem. Moreover, the complexity of related resource leveling problems sharing the same objective is studied. These results lead to polynomial or pseudo-polynomial algorithms or NP-hardness proofs, allowing for an interesting comparison with classical machine scheduling problems. [ABSTRACT FROM AUTHOR]

Nowadays, smart devices are becoming more prevalent in industrialized countries generating requests that require computational processing. Recently, collaborative edge-fog-cloud computing networks have been developed to allocate users' requests to computing resources. However, scheduling these requests while accounting for user requirements and limited resources remains a challenge. This study proposes a structured planning at multiple levels in a collaborative edge-fog-cloud environment to allocate and schedule requests, aiming to reduce network latency. So, an Integer Programming (IP) formulation is developed to minimize network latency for users. Some network limitations are considered in the model, such as network logic for directing requests to computational resources, meeting deadline and nodes capacity constraints. Additionally, constraints related to processing allowable workload volume are integrated into the model. This strategy changes the workload distribution among the edge, fog, and cloud layers to approximately 24%, 27%, and 47%, respectively, creating a more balanced workload distribution and reducing workload traffic. Other results indicate that simply increasing the computational capacity of the fog nodes does not always improve network performance. This suggests the need for a more analytical approach, considering additional factors simultaneously in the underlying network. These outcomes underscore the efficiency and practical significance of the proposed model in a collaborative edge-fog-cloud computing landscape. The findings can help cloud service enterprises in providing efficient services for addressing the request scheduling and allocation challenges in edge-fog-cloud networks. [ABSTRACT FROM AUTHOR]

This paper presents an innovative scheduling algorithm designed specifically for real-time energy harvesting systems, with a primary focus on minimizing energy consumption and extending the battery's lifespan. The algorithm employs a fixed priority assignment which is the deadline monotonic policy, we have chosen it for its optimality and superior performance compared to other fixed priority scheduling methods. To achieve a balance between energy efficiency and system performance, we incorporated a DVFS (Dynamic Voltage and Frequency Scaling) technique into the algorithm. This adaptive approach enables precise control over the processor's operating frequency, effectively managing energy consumption while ensuring satisfactory system functionality. The core objective of our scheduling algorithm centers on optimizing energy utilization in real-time energy harvesting systems, specifically tailored to extend the battery's operational life. Rigorous evaluations, including comprehensive comparisons against established fixed priority scheduling algorithms, validate the algorithm's efficacy in significantly reducing energy consumption while preserving the system's overall functionality. By combining the deadline monotonic policy and DVFS technique, our proposed algorithm emerges as a promising solution for energy-autonomous systems, contributing to the advancement of sustainable energy practices in real-time applications. As energy harvesting technologies continue to progress, our algorithm holds valuable potential to provide critical insights for enhancing the efficiency and reliability of future energy harvesting systems. [ABSTRACT FROM AUTHOR]

Copyright of Umsatzsteuer-Rundschau is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In light of the rapidly growing and advancing Internet of Things (IoT) technology, delay sensitive tasks, deadline aware tasks, and power intensive IoT applications are on the rise, the adoption of fog computing has emerged as a promising solution for problems in IoT technology. Task offloading in the Edge-Fog-Cloud environment is difficult due to the inherent limits of IoT devices in terms of computing and storage capacity, the diversity of fog servers, and the varying characteristics of IoT tasks, such as their sensitivity to delays. However, the most challenging issue is finding the best suitable device to compute the task within the deadlines, reduce the total power consumption and minimize the computation time. The proposed IOTD algorithm aims to meet the deadlines of all tasks while minimizing the total computation time and energy consumption. The results of simulation experiments confirmed that the proposed method improves the reliability of meeting deadlines, total execution time, utilization of fog devices and total energy, compared with the state-of-the-art algorithms: Artificial Bee Colony and Osmotic Approach. [ABSTRACT FROM AUTHOR]

Cloud computing has become popular for small businesses due to its cost-effectiveness and the ability to acquire necessary on-demand services, including software, hardware, network, etc., anytime around the globe. Efficient job scheduling in the Cloud is essential to optimize operational costs in data centers. Therefore, scheduling should consider assigning tasks to Virtual Machines (VMs) in a Cloud environment in such a manner that could speed up execution, maximize resource utilization, and meet users' SLA and other constraints such as deadlines. For this purpose, the tasks can be prioritized based on their deadlines and task lengths, and the resources could be provisioned and released as needed. Moreover, to cope with unexpected execution situations or hardware failures, a fault-tolerance mechanism could be employed based on hybrid replication and the re-submission method. Most of the existing techniques tend to improve performance. However, their pitfall lies in certain aspects such as either those techniques prioritize tasks based on a singular value (e.g., usually deadline), only utilize a singular fault tolerance mechanism, or try to release resources that cause more overhead immediately. This research work proposes a new scheduler called the Deadline and fault-aware task Adjusting and Resource Managing (DFARM) scheduler, the scheduler dynamically acquires resources and schedules deadline-constrained tasks by considering both their length and deadlines while providing fault tolerance through the hybrid replication–resubmission method. Besides acquiring resources, it also releases resources based on their boot time to lessen costs due to reboots. The performance of the DFARM scheduler is compared to other scheduling algorithms, such as Random Selection, Round Robin, Minimum Completion Time, RALBA, and OG-RADL. With a comparable execution performance, the proposed DFARM scheduler reduces task-rejection rates by 2.34–9.53 times compared to the state-of-the-art schedulers using two benchmark datasets. [ABSTRACT FROM AUTHOR]

The operational side of aircraft maintenance plays a key role in ensuring smooth workflow and the practiced processes' reliability in aircraft maintenance repair and overhaul (MRO). Correct MRO planning effectively reduces downtime and enhances the overall accuracy and dependability of procedures. An optimal schedule of maintenance resources and capacity, which entails using the least amount of ground time and schedule modifications, can be used to ensure that all activities are completed productively and consistently before their deadlines. This paper delves into the creation of an optimization approach that considers the dynamic environment of aircraft maintenance. The approach deals with a constraint programming (CP) model for heavy maintenance resources and capacity scheduling, in which variables such as scheduled maintenance tasks, ground time, workload distribution, and maintenance due dates are bounded by resources and technicians' availability. The proposed CP model showed a strong ability to satisfy different constraints and conditions that were introduced in the artificial industrial environment within a short solving time of 3.12 seconds. Thus, it showed promising results in the case of further developments. [ABSTRACT FROM AUTHOR]

Editor's Note: Readers of this journal are encouraged to submit news items on awards, appointments, and other activities about themselves or their colleagues. Deadline dates for news and notices are 2 months prior to publication. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article explores the establishment and significance of the journal "Medizinische Genetik" in Germany. It focuses on the contributions of Prof. Dr. Jan Murken to the field of medical genetics and the challenges faced in creating the journal. The article emphasizes the importance of the journal as a platform for communication and exchange among geneticists, as well as its role in promoting the recognition and establishment of medical genetics as a field. It also discusses the topics covered in the journal, such as the role of medical geneticists, quality assurance in laboratories, ethical issues, and legal considerations related to genetic testing. The article also mentions the transition of the journal to different publishers and its recent inclusion in PubMedCentral. Additionally, it briefly mentions the personal interests and activities of Prof. Dr. Jan Murken, including their involvement in a museum dedicated to King Otto of Greece. [Extracted from the article]

This article discusses the fast-approaching deadline for sponsors to transition ongoing clinical trials in the EU/European Economic Area from the Clinical Trials Directive 2001/20/EC to the Clinical Trials Regulation 536/2014. In particular, the authors discuss the medical writer's crucial role in ensuring that documentation meets the regulation harmonisation and transparency requirements; they also highlight challenges seen when redacting commercially confidential information in the preparation of transition applications. [ABSTRACT FROM AUTHOR]

Flows over time are a natural way to incorporate flow dynamics that arise in various applications such as traffic networks. In this paper we introduce a natural variant of the deterministic fluid queuing model in which users aim to minimize their costs subject to arrival at their destination before a pre-specified deadline. We determine the existence and the structure of Nash flows over time and fully characterize the price of anarchy for this model. The price of anarchy measures the ratio of the quality of the equilibrium and the quality of the optimum flow, where we evaluate the quality using two different natural performance measures: the throughput for a given deadline and the makespan for a given amount of flow. While it turns out that both prices of anarchy can be unbounded in general, we provide tight bounds for the important subclass of parallel path graphs. [ABSTRACT FROM AUTHOR]

THE PASSAGE OF TIME AND GEOPOLITICAL AND STRATEGIC CHANGES WORLDWIDE AND BEYOND, HAVE OUTLINED SEVERAL PICTURES OF THE SAME SOCIAL REALITY, SO THAT IT WAS NECESSARY FOR THE LEGISLATOR TO INTERVENE AND PROTECT FAMILY SOCIAL RELATIONS AGAINST DEVIANT BEHAVIORS, CRIMINALIZING THE ACT OF FAMILY ABANDONMENT. THUS, THE VICISSITUDES OF TIME, THE SHORTCOMINGS OF COMMUNITIES AND OF THE INDIVIDUAL IN HIS OWN PERSON, TOOK HIM OUT OF THE NORMAL PATH OF A PEACEFUL LIFE AND THREW HIM INTO THE TUMULT OF STORMS TRIGGERED BY PSYCHOLOGICAL TRAUMAS, REPRESSED NEEDS, MATERIAL LACKS AND CRIMES OF ALL KINDS. [ABSTRACT FROM AUTHOR]

We investigate a matrix analysis study for a single-server Markovian queue with finite capacity, i.e. an M/M/1/N queue, where the single server can go for a maximum, i.e. a K number of consecutive vacation periods. During these vacation periods of the server, every customer becomes impatient and leaves the queues. If the server detects that the system is idle during service startup, the server rests. If the vacation server finds a customer after the vacation ends, the server immediately returns to serve the customer. Otherwise, the server takes consecutive vacations until the server takes a maximum number of vacation periods, e.g. K, after which the server is idle and waits to serve the next arrival. During vacation, customers often lose patience and opt for scheduled deadlines independently. If the customer's service is not terminated before the customer's timer expires, the customer is removed from the queue and will not return. Matrix analysis provides a computational form for a balanced queue length distribution and several other performance metrics. We design a 'no-loss; no-profit cost model' to determine the appropriate value for the maximum value of K consecutive vacation periods and provide a solution with a numerical illustration. [ABSTRACT FROM AUTHOR]

Copyright of MedR Medizinrecht is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Editor's Note:Readers of this journal are encouraged to submit news items on awards, appointments, and other activities about themselves or their colleagues. Deadline dates for news and notices are 2 months prior to publication. [ABSTRACT FROM AUTHOR]

With the rise of information-centric networks (ICN), the user can access the caching content from nearby caching nodes rather than the remote content server through in-network caching facilities. The existing articles did not look into minimum caching content retrieval latency, user energy consumption, user financial cost, and maximum service provider profit-aware proper resource orchestration and job scheduling policies all at once, taking into account multiple publishers and subscriber-based caching jobs, blockchain, user-owned and service provider cache, pre-caching, neighbor collaboration, and available resources. To suppress these challenges, this article offers a proactive pre-caching, collaboration, blockchain, minimum latency-aware resource, and job scheduling policy for 6G ICN services that takes into account publishers’ and subscribers’ caching job requests, user-owned and service provider cache, available resources, and deadline. The experimental results visualized that at least 16% content retrieval delay, 66% energy, and 22% service provider profit gain are attained in the proposed scheme over the existing techniques. [ABSTRACT FROM AUTHOR]

Edge computing is a promising technology, especially for offloading users' computationally heavy tasks. The close proximity of edge computing units to users minimizes network latency, thereby enabling delay-sensitive applications. Although optimal resource provisioning and task offloading in edge computing are widely studied in the literature, there are still some critical research gaps. In this study, we propose a traffic-aware optimal association and task-offloading approach. The proposed method does not rely solely on the average rate of offloading requests, which can differ from actual values in real time. Instead, it uses an intelligent, high-precision prediction model to forecast future offloading requests, allowing resource provisioning to be based on future sequences of requests rather than average values. Additionally, we propose an optimization-based approach that can meet task deadlines, which is crucial for mission-critical applications. Finally, the proposed approach distributes the computing load over multiple time steps, ensuring future resource scheduling and task-offloading decisions can be made with a certain level of flexibility. The proposed approach is extensively evaluated under various scenarios and configurations to validate its effectiveness. As a result, the proposed deep learning model has resulted in a request prediction error of 0.0338 (RMSE). In addition, compared to the greedy approach, the proposed approach has reduced the use of local and cloud computing from 0.02 and 18.26 to 0.00 and 0.62, respectively, while increasing edge computing usage from 1.31 to 16.98, which can effectively prolong the lifetime of user devices and reduce network latency. [ABSTRACT FROM AUTHOR]

In European governance, transnational administrative networks have become crucial for addressing complex cross‐border challenges, supplementing formal harmonization efforts with informal collaborative mechanisms. However, little attention has been given to their role in addressing super‐wicked policy problems, where formal structures and political prioritization are lacking despite pressing temporal deadlines and substantial costs for future generations. This study investigates the drivers of administrative coordination in Europe within such contexts, using the case of antimicrobial resistance as a case study. Employing a mixed‐method approach, including Logistic Regression Quadratic Assignment Procedure and a qualitative analysis based on originally collected expert survey and interview data, the research uncovers a notable trend: countries with similar levels of institutional quality are four times more likely to establish connections. This selective coordination dynamic is shaped by domestic circumstances, politico‐administrative considerations, and the pursuit of pragmatic, implementable policy solutions, with important implications for the ongoing struggle against antibiotic resistance. [ABSTRACT FROM AUTHOR]

Offloading some tasks from the local device to the remote cloud is one of the important methods to overcome the drawbacks of the medical mobile device, such as the limitation in the execution time and energy supply. The challenges of offloading task is how to meet multiple requirement while keeping energy-saving. We classify tasks in the medical mobile device into two kinds: the first is the task that hopes to be executed as soon as possible, those tasks always have a deadline; the second is the task that can be executed anytime and always has no deadlines. Past work always neglects the energy consumption when the medical mobile device is charged. To the best of our knowledge, this paper is the first paper that focuses on the energy efficiency of charging from a power grid to a medical device during work. By considering the energy consumption in different locations, the energy efficiency during working and energy transmission, the available energy of and the battery, we propose a scheduling method based on DQN. Simulations show that our proposed method can reduce the number of un-completed tasks, while having a minimum value in the average execution time and energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, a network comprising wireless devices equipped with buffers transmitting deadline-constrained data packets over a slotted-ALOHA random-access channel is studied. Although communication protocols facilitating retransmissions increase reliability, a packet awaiting transmission from the queue experiences delays. Thus, packets with time constraints might be dropped before being successfully transmitted, while at the same time causing the queue size of the buffer to increase. To understand the trade-off between reliability and delays that might lead to packet drops due to deadline-constrained bursty traffic with retransmissions, the scenario of a wireless network utilizing a slotted-ALOHA random-access channel is investigated. The main focus is to reveal the trade-off between the number of retransmissions and the packet deadline as a function of the arrival rate. Towards this end, analysis of the system is performed by means of discrete-time Markov chains. Two scenarios are studied: (i) the collision channel model (in which a receiver can decode only when a single packet is transmitted), and (ii) the case for which receivers have multi-packet reception capabilities. A performance evaluation for a user with different transmit probabilities and number of retransmissions is conducted. We are able to determine numerically the optimal probability of transmissions and the number of retransmissions, given the packet arrival rate and the packet deadline. Furthermore, we highlight the impact of transmit probability and the number of retransmissions on the average drop rate and throughput. [ABSTRACT FROM AUTHOR]

Efficiently scheduling heterogeneous cloud workloads in modern computing environments is a formidable challenge. Managing diverse resource needs, intricate task dependencies, and optimizing resource allocation are key concerns in this context. To address these challenges, this paper introduces a novel approach that combines the Advantage Actor-Critic (AAC) and Proximal Policy Optimization (PPO) models. AAC accurately estimates task dependencies, while PPO optimizes task scheduling operations. The proposed model leverages essential parameters such as CPU utilization, memory requirements, storage demands, network bandwidth, task deadlines, and task dependencies. The results of experiments conducted demonstrate the efficacy of the proposed approach. It achieves a 4.9% reduction in makespan, indicating faster task completion, an 8.5% increase in resource utilization, showcasing improved resource efficiency, a 2.5% reduction in energy requirements, extending the cloud deployment lifespan, and a 4.9% increase in the deadline hit ratio, highlighting the model's proficiency in meeting task deadlines. In conclusion, the novel scheduling approach, integrating AAC and PPO models, offers substantial advantages over existing methods. Precise task dependency estimation and optimized scheduling lead to enhanced performance metrics, including reduced makespan, augmented resource utilization, increased throughput, and improved deadline adherence. These findings have promising implications for real-world applications, underscoring the solution's potential impact on contemporary computing environments. [ABSTRACT FROM AUTHOR]

As more and more workflow applications are deployed in the cloud, how to optimize resource scheduling cost while satisfying workflow deadline constraints is a popular research area. In this paper, we propose a cost optimization with deadline constraint (CODC) algorithm. Firstly, workflow tasks are merged to reduce the data transfer overhead between different instances. Secondly, paying attention to the impact of parent and subtasks on the priority of the current task, the case of unmet task sub-deadlines is considered to select the instance that completes task execution earliest. Finally, the experimental results are compared with the existing algorithms on five workflows. CODC algorithm has a lower workflow execution cost compared to other three comparison algorithms. [ABSTRACT FROM AUTHOR]

While deadlines are routinely used as a strategy to achieve goals, the way these deadlines are described has been relatively overlooked in terms of their influence on decision‐making. We examined the unique effect of future (vs. present) anchors in deadlines on ones' goal achievement. A field study shows that a deadline with a future anchor encourages people to complete a task more effectively within the deadline than a deadline with a present anchor (Study 1). In Study 2, we further demonstrated the positive effect of future (vs. present) anchors on individuals' goal achievement in a more controlled lab setting. Study 3 provided support for our mechanism by examining individuals' perceived time availability until the deadline underlies this effect. Studies 4 and 5 delved deeper, investigating our mechanism by examining individuals' dispositional time value as an important moderator. This research enriches our understanding of time perception and decision‐making, and offers valuable insights on how to strategically deal with deadlines to enhance one's goal achievement. [ABSTRACT FROM AUTHOR]

Edge computing provides task offloading services to extend the computational capacity of mobile users and reduce task latency. Prior studies mainly focus on tasks with strict deadlines. However, in the real world, some tasks may not always have to be finished before hard deadlines, e.g., multimedia tasks. Tasks with soft deadlines can miss their primary deadlines, but not by too much, and still be timely. This has not been properly considered by existing offloading approaches. In this paper, we propose CONFECT, a novel offloading approach that handles tasks with mixtures of hard deadlines and soft deadlines. Specifically, we first formulate the problem as an integer linear programming and prove its hardness. Then, we propose two online algorithms with proven competitive ratios to solve the problem collectively, including an algorithm that assigns tasks to edge servers to maximize the task completion ratio and an algorithm that adjusts the task execution order to maximize the task completion revenue. Moreover, to balance the fairness among tasks and system revenue elastically, we extend CONFECT by using a tunable fairness knob. Finally, extensive experiments show that CONFECT outperforms five baseline algorithms in terms of task completion ratio and completion revenue. [ABSTRACT FROM AUTHOR]

Individuals often have difficulties completing tasks in a timely manner. Whether it be scheduling a doctor's appointment, purchasing a birthday gift, or booking an airline ticket, waiting until the very last minute can carry serious consequences. In two experimental studies, we explore how individuals can be encouraged to finish tasks promptly. We provide evidence for the "fresh start effect" by showing that a temporal landmark signaling a new beginning helps speed up their task completion. Notably, we demonstrate that the "fresh start nudge" can facilitate early task completion through the underlying processes of meaningfulness and motivation. We also report the moderating role of task openness, supporting the claim that the fresh start effect is stronger when the task is perceived to be relatively closed (rather than open) and must therefore be completed in fewer steps without interruption. [ABSTRACT FROM AUTHOR]

A looming deadline. A difficult situation at home. A heated phone conversation that redirects our attention. Certain features of our circumstances can be (at least partially) excusing; sometimes, agents who act wrongly in the face of circumstantial pressures are not (that) blameworthy for having done so. But we're rather bad at detecting these factors that excuse others from blame. When put together, these two observations yield an under-appreciated problem: we fall short of procedural norms of blame in fairly systematic ways. [ABSTRACT FROM AUTHOR]

Copyright of Revista Crítica de Relaciones de Trabajo, Laborum is the property of Ediciones Laborum S.L. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the MAXSPACE problem, given a set of ads A, one wants to place a subset A′ ⊆ A into K slots B1, ..., BK of size L. Each ad Ai ∈ A has sizesi and frequencywi. A schedule is feasible if the total size of ads in any slot is at most L, and each ad Ai ∈ A′ appears in exactly wi slots. The goal is to find a feasible schedule that maximizes the space occupied in all slots. We introduce MAXSPACE-RDWV, a MAXSPACE generalization with release dates, deadlines, variable frequency, and generalized profit. In MAXSPACE-RDWV each ad Ai has a release date ri ≥ 1, a deadline di ≥ ri, a profit vi that may not be related with si and lower and upper bounds wmini and wmaxi for frequency. In this problem, an ad may only appear in a slot Bj with ri ≤ j ≤ di, and the goal is to find a feasible schedule that maximizes the sum of values of scheduled ads. This paper presents some algorithms based on meta-heuristics GRASP, VNS, and Tabu Search for MAXSPACE and MAXSPACE-RDWV. We compare our proposed algorithms with Hybrid-GA proposed by Kumar et al. [Eur. J. Oper. Res. 173 (2006) 1067–1089]. We also created a version of Hybrid-GA for MAXSPACE-RDWV and compared it with our meta-heuristics. Some meta-heuristics like VNS and GRASP+VNS have better results than Hybrid-GA for both problems. In our heuristics, we apply a technique that alternates between maximizing and minimizing the fullness of slots to obtain better solutions. We also applied a data structure called BIT to the neighborhood computation in MAXSPACE-RDWV and showed that this enabled our algorithms to run more iterations. [ABSTRACT FROM AUTHOR]

Copyright of Revista Româna de Dreptul Muncii is the property of Wolters Kluwer Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Majalat Monazaat Al-Aamal is the property of Majalat Monazaat Al-Aamal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)