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During the real production system, the scheduling scheme change is mostly changed by dynamic events or new tasks. Due to the different urgency degrees of dynamic events, the corresponding scheduling methods should be adopted to ensure the production efficiency of enterprises. In this paper, an event-driven dynamic workshop scheduling model is established based on Ant Colony System (ACS), and two scheduling methods are designed to deal with dynamic events, namely parallel scheduling and parallel priority scheduling, respectively. The goal of parallel scheduling is to minimize the total makespan, while that of parallel priority scheduling is to minimize the delivery time of dynamic events. Additionally, a selective scheduling strategy is designed to determine the optimal scheduling method according to the urgency degree of dynamic events. Finally, the feasibility of the selective scheduling strategy in solving the dual-objective dynamic job shop scheduling problem (DJSP) is verified by an example experiment on DJSP as well as a large scale problem test set.

The Government of India launched the Smart City concept in 2015. The Swachha Bharat Abhiyaan is the mission under the same theme. To make these developing cities to be actually smart it is very important that the cities should be clean, and the surrounding should be hygienic. The main part in cleanliness is the garbage collection system that has to be smarter. In addition to this the people need easy accessibility to the garbage disposing points and garbage collection process has to be efficient in terms of time and cost in terms of fuel. In the present situation, the Garbage collection in the Indian cities is mostly manual where all the workers need to go to location where garbage is dropped by the citizens, and they need to collect it with some equipment. This system is not automated till now. The smart cities in India are developing with the fast rate in many respects such as smart homes, connectivity with world by roads, rails and airways, use of information technology etc. However, there is need to improve the garbage collection and disposal in smart cities. The present paper discusses the various issues with existing garbage collection systems and the novel approach to improve the garbage collection using Automated Robotic Dustbin (ARD).

Meeting tail latency Service Level Objective (SLO) as well as achieving high resource utilization is important to distributed storage systems. Recent works adopt strict priority scheduling or constant rate limiting to provide SLO guarantee but cause under-utilization resources. To address this issue, we first analyze the relationship between workload burst and latency SLO. Based on burst patterns and latency SLOs, we classify tenants into two categories: Postponement-Tolerable tenant and Postponement-Intolerable tenant. We then explore the opportunity to improve resource utilization by carefully allocating resources to each tenant type. We design Rate-Limiting-Priority scheduling algorithm to limit the impact of high priority tenants on low priority ones. Meanwhile, we propose Postponement-Aware scheduling algorithm which allows Postponement-Intolerable tenants to preempt system capacity from Postponement-Tolerable tenants. This helps to increase resource utilization. We propose a latency SLO guarantee framework Gecko. Gecko guarantees multi-tenant latency SLOs via combining the two proposed scheduling algorithms together with an admission control strategy. We evaluate Gecko with real production traces and the results show that Gecko admits 44% more tenants on average than state-of-the-art techniques meanwhile guaranteeing latency SLO.

In Vitro Diagnosis devices (IVD) are made of hundreds of parts. At present, time-slot scheduling method makes some parts of the device idle when they are working. Therefore, this paper proposes an emergency priority scheduling optimization algorithm. Firstly, a linear programming method is used to obtain the optimal solution with optimization objective of the shortest duration. When emergency samples arrive, rescheduling is used to ensure the effectiveness of the test. Taking a fluorescence immunoassay as an example, the time-slot scheduling method and emergency priority scheduling optimization algorithm are compared. The results show that he latter is 3/min faster than that of the former. When an emergency is added, the latter produces results in time. The proposed method has at least 12 hours MTBF (Mean Time Between Failure). This proves that the proposed method is more effective.

Many cyber-physical applications consist of chains of tasks. Such tasks are often loosely dependent, meaning task execution is time-triggered and independent of the update rate of input data. Since meaningful output can be obtained after processing all the intermediate tasks of a chain, the end-to-end latency of the chain is an important metric that can affect the correctness and quality of the system. In this paper, we present a chain-based fixed-priority preemptive scheduler for multicore real-time systems. The scheduler identifies effective chain instances contributing to the generation of updated output, and employs a runtime policy to improve the end-to-end latency of chains. Based on our scheduler, an analysis method is proposed with two parts: (i) bounding the start and finish time of each job, and (ii) analyzing the end-to-end latency of effective chain instances. Experimental results show that our chain-based scheduler achieves up to 83% reduction in end-to-end latency compared to the state-of-the-art and yields a significant benefit in inter-chain distance over chain-unaware schedulers. Furthermore, our analysis method can be easily adapted to chain-unaware schedulers and provides tighter bounds than prior work.

International audience; The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). Data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing.

The Robot Operating System (ROS) is widely adopted for the control of the robots which is usually running on the Linux system with a standard kernel. But snake robot has a high requirement for the real-time of the control framework since it has several modules which contain a variety of sensors. To solve the problem, the control frame based on the Xenomai real-time system is developed by inserting a patch to the kernel and the real-time performance can be further enhanced by priority scheduling. The proposed control framework of the snake robot is applied to deal with the non- real-time communication thread with ROS packages and the real-time communication thread with the joint controller. So the application programs based on the proposed control framework can realize the multi-task function and be compatible with the ROS system. At last, the experiments validate that the control framework can control the hardware of the snake robot efficiently.

Together with Fixed Priority Scheduling, Earliest Deadline First (EDF) is the second leg on top of which a large portion of the research on real-time systems stands. At the heart of the EDF exact schedulability test, a pivotal role is played by the demand bound function test, which requires to evaluate whether the amount of work required to complete a set of jobs is less than or equal to the amount of time available. Such a test is checked over a set of time instants. In this paper, it is proposed a method that cuts drastically the set of constraints to be checked. Such a method is applicable also to tasks with release offset. Notably, it is proved that such a reduced set of constraints is minimal: it is not possible to reduce the set any further without losing the sufficiency of the test. The code to make this reduction is publicly available on GitHub.

Software defined wide area network (SD-WAN) is a promising technique in scheduling mix-flows with different quality of service (QoS) requirements due to the decoupling of control and data planes, if the flow information, e.g., the flow size, is pre-known. In many cases, it is almost impossible to pre-know the flow size of the mix-flows before the transmission starts. In this context, how to efficiently schedule the information-agnostic mix-flows to provide SD-WAN users with better quality of experience (QoE) is a burning problem. In this paper, we propose an information-agnostic mix-flows scheduling mechanism, called QIMS, to minimize flow completion time (FCT). QIMS leverages a two stage priority scheduling mechanism, which is implemented on both the SD-WAN users and switch side. In the first stage, nondeadline flows are assigned with the highest priority, while each deadline flow is assigned with the lowest priority at its start and its priority is gradually promoted according to its urgency. More specifically, a flow is more urgent if it is closer to its deadline. In the second stage, QIMS keeps the priority of non-deadline flows unchanged, but reverses each deadline flow's priority which is assigned in the first stage to avoid packet reordering. Furthermore, we formulate the relationship between priority and urgency as a nonlinear sum-of-ratios problem with queuing theory, and derive the optimal priority promotion thresholds. The experiment results show that QIMS reduces the average FCT by 18% and improves the system throughput by 8.9%.

This paper proposes a priority-based monitoring system in Data Distribution Service environment where messages are frequently transmitted and received. The proposed system uses parallel processing and priority-based algorithm to perform monitoring and message error detection in real-time. The proposed system is described by algorithm and its performance is verified through simulation.

Traffic congestion has a significant negative impact on the accelerating pace of daily human activities. Traffic jams increase the transportation costs for goods and humans. They are also amongst the leading factors for pollution in the atmosphere and consequently increase health risks for the population. One way to reduce the amount of emissions produced by vehicles in traffic jams is to mitigate traffic congestion and promote the usage of public transportation. In this paper, we propose a solution that establishes on-demand, virtual bus lanes to prioritize public transportation over other traffic and provide detour guidelines for other drivers, while causing insignificant detour penalties. Our solution leverages incremental window aggregations to identify the busiest road segments, priority scheduling, and Dijkstra shortest path algorithm to shape and detour traffic. Our experimental evaluation shows the effectiveness of our Environment Protective Traffic Orchestration (EPTrOn) algorithm in identifying and alleviating traffic congestions.

Arranging is a champion among the basic deeds of the system head, which is responsible to take decision in order to pick the technique in readied line that will be consigned to CPU. There are various sorts of arranging computations for decision making system. Priority Scheduling Algorithm relies upon need named to every method. In this paper, booking estimation is recycled so that in similar type concepts the shortest job first count is used.

Our everyday lives depend on affordable energy services to function smoothly and to develop equitably [1]. But now the dosage of renewable energy is very low. The grid-connected generation of renewable energy is very important, but it is not economically efficient and needs policy support. The current power operation management system has not adapted to the development of renewable energy. On this study, the researchers designed a Renewable Energy Information Management System to improve the technical level for the operation and dispatch of renewable energy, and promote the use of renewable energy. This design classifies energy sources. It can support different tax rates from different energy sources and can support government subsidies and environmental protection subsidies. This system also designs priority scheduling to promote the development of renewable energy. It is designed by the framework of Browser/Server and is developed by J2EE. This design of a renewable energy information management system can be used by Power Supply Company. It can guide the company to improve the use of renewable energy.

In service function chaining, data flows from a particular application or user travel along a pre-defined sequence of network functions. Appropriate service function chaining resource allocation is required to comply with the service level required by the application. In this paper, we introduce a dynamic priority assignment for flows that compete for service using a particular network function in a chain. Using the recent results of the performance metrics of transient birth-death processes, we analyse this priority assignment and develop an optimal strategy for selecting a (cheap) low-or (expensive) high-priority service, given the flow's service level agreement requirements. A decision table can, thus, be created to facilitate the fast, online priority scheduling of newly arriving flows requesting service. © 2019 IEEE.

This paper focuses on priority based processing of streaming data. One of the greatest challenges in big data analytics is responding to a bursty input load. The common solutions are to use dynamic resource provisioning techniques, however, these techniques may not respond quickly enough to the change in the load. Another option is to overprovision, but this results in wasted computing resources. This paper describes a technique that can be used in cases where resources are statically provisioned. This technique enables users to prioritize certain input data items so that in cases where the load suddenly increases, the high priority items are given precedence over low priority items. This technique is implemented on the Spark Streaming engine.

The transmission delay of CAN gateway message will affect the real-time performance of the whole CAN network. Firstly, the transmission delay model of CAN gateway message is analyzed in this paper. After two algorithms, ID Priority Scheduling (IDPS) algorithm and Earliest Deadline First (EDF) algorithm, are compared, the Dynamic Feedback based Hybrid Priority Scheduling (DFHPS)algorithm is proposed with implementation algorithm. Finally, Hardware In Loop (HIL) simulation test is conducted. The test results show that this method can effectively reduce the delay of high-priority messages when the network load rate is high, and ensure that low-priority messages can also get good response time.

The calculation of massive data has always been an significant research topic, and the data calculation job scheduling system is an important platform to deal with this problem. There are many scheduling systems in use, however, their functions are not perfect and cannot meet the needs of users. This paper designs a novel architecture of scheduling system for big data framework. The system has basic scheduling functions, such as algorithm job assignment, job priority scheduling, job result management. It supports three algorithms to schedule jobs: scheduling-in-order algorithm, scheduling-by-priority algorithm, and scheduling-by-resource algorithm, meanwhile implementing dynamic switching of these algorithms. The system has a graphical interface which greatly facilitates interaction between the system and the user.

The success indicator of an institution and company is when the available resources have been used effectively and efficiently. The limited resources available can cause delays in ongoing business processes. The delay in a business process is always closely related to financing. The slower the business process runs, the more the expenses that must be incurred for labor and material costs. Therefore, it is necessary to optimize the cost and time for an organization. It can be minimized by using goal programming. In goal programming, weights are given to make sense of which activities to be watched. In this research using lingo software to calculate goal programming.

Consider uniprocessor platforms, the scheduling of synchronous implicit deadline periodic task sets and the dual priority scheme where each task is assigned two fixed priorities. That is, at run time each task starts executing using its primary priority and is promoted if not completed at an intermediate deadline. We present counter-intuitive examples illustrating how difficult this scheduling problem is. We propose a preprocessing approach to remove from the scheduling problem lowest priority viable tasks as defined by Audsley's procedure. We revisit one solution called RM + RM conjectured optimal. We propose a procedure to compute promotion deadlines based on multiple simulations over an hyperperiod called FDMS. That solution has an exponential time complexity but an experimental success ratio of 100%. Then we propose a new sub-optimal solution to assign priorities called 1 /RM + RM along with a very simple promotion deadline assignment scheme called RML for which no simulation are required, the procedure is simple and the success ratio is close to 99.99%. We show that the method is fast and scalable to very large task sets which makes it ideal for practical applications., info:eu-repo/semantics/published

This paper considers Dual Priority (DP) scheduling of constrained deadline sporadic tasks on uniprocessor. The initial fixed priority of each job of a task is promoted to a higher priority (called, promoted priority) after a fixed time interval (called, promotion point) relative to the release time of that job. DP scheduling alters the default preemptive behavior of traditional fixed priority (FP) scheduling to efficiently utilize the processor as close as possible to that of the optimal earliest deadline first (EDF) scheduler. In this paper, we address some of the main challenges of DP scheduling including derivation of a sufficient schedulability test, determination of promotion point of each task1. To the best of our knowledge, this test is the first schedulability test for DP scheduling applicable to constrained deadline sporadic tasks. The test is applicable for any given promotion points of the tasks and has pseudo-polynomial time complexity. We also propose two different heuristics to assign the promotion points, and experimental results show that the proposed test achieves performance very close to that of EDF scheduling.

This paper considers the problem of design optimization for real-time systems scheduled with fixed priority, where task priority assignment is part of the decision variables, and the timing constraints and/or objective function linearly depend on the exact value of task response times (such as end-to-end deadline constraints). The complexity of response time analysis techniques makes it difficult to leverage existing optimization frameworks and scale to large designs. Instead, we propose an efficient optimization framework that is three magnitudes (1,000×) faster than Integer Linear Programming (ILP) while providing solutions with the same quality. The framework centers around three novel ideas: (1) An efficient algorithm that finds a schedulable task priority assignment for minimizing the average worst-case response time; (2) The concept of Maximal Unschedulable Deadline Assignment (MUDA) that abstracts the schedulability conditions, i.e., a set of maximal virtual deadline assignments such that the system is unschedulable; and (3) A new optimization procedure that leverages the concept of MUDA and the efficient algorithm to compute it.

At present, the safety computer use the first come first served (FCFS) approach to transmit data stream, but this method does not distinguish the data stream types, which may cause safety critical data stream transmission timeout. In order to meet the communication requirements of the future safety computer, by analyzing the characteristics of communication data and the main factors influencing real-time performance in the safety computer, the constrained multilevel priority scheduling strategy is proposed. This strategy consider the constraint relation between data frames under the conditions of meeting the multi-priority when the data to be transmitted. Through the network calculation method, the communication delay of different data stream in the two modes is analyzed. The improved scheduling strategy shortens the maximum delay of emergency safety critical data stream by 22.5% in the current safety computer under the condition of increasing the limited delay of non-safety critical data stream, so the strategy can effectively improve communication real-time performance of emergency safety critical data stream.

Fixed-priority scheduling with preemption thresholds (FPTS) is supported by the AUTOSAR and OSEK standards as a scheduling policy. Since FPTS is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority nonpreemptive scheduling (FPNS), it aims to improve schedulability. In this paper, we prove, as an intermediate step towards the exact best-case response time analysis for FPTS, that the best-case computation time of a non-preemptive task scheduled under FPTS or FPNS is a tight lower bound for its response time. In addition, we illustrate by means of an example that the best-case response time analysis for FPTS is most likely not a straight forward extension of the current best-case analysis for FPPS.

Priority scheduling among running transactions is one of the most important issues in the design of mobile distributed real-time database systems (MDRTDBS). In MDRTDBS, to perform correct transaction scheduling, several priority heuristics with different concurrency control methods are used, so that, it could minimize transaction abort rate. Priority heuristic approaches deal with problem of assigning the priorities among transactions so that it could helpful with concurrency control (CC) mechanism to achieve typical time constraint. In recent few years, the performance of CC protocols of distributed real-time database Systems (DRTDBS) have been examined using different priority heuristics methods. However, very few numbers of approaches have been proposed on priority heuristics for wireless environments. Hence, a new priority heuristic method based on deadline computed by considering write only operations has been proposed for wireless environments. It is improved priority heuristic approach which shows better results than earlier priority heuristics. In recent years, researchers have classified the transactions in two types called as Read only transactions (ROT) and Update transaction. This new priority heuristic for mobile environment considers ROT and update transactions separately. Further, a study has also been done to examine the impact of these priority heuristics as compared with number of locks and mixed method approaches.

Scheduling real-time applications on general purpose multicore platforms is a challenging problem from a timing analysis perspective. Such platforms expose uncontrolled sources of interference whenever concurrent accesses to memory are performed. The non-deterministic bus and memory access behavior complicates the estimations of applications' worst-case execution times (WCET). The 3-phase task model seems a good candidate to circumvent the uncontrolled sources of interference by isolating concurrent memory accesses. A task is divided in three successive phases; first, the task loads its instruction and data in a local memory, then it executes non-preemptively using those pre-loaded instructions and data, and finally, the modified data are pushed back to main memory. Following this execution model, tasks never access the bus during their execution phase. Instead, all the bus accesses are performed during the first and third phases. In this paper, we focus on the global fixed-priority scheduling of the 3-phase task model. A new schedulability test is derived by modelling the interference happening on the bus rather than the interference on the cores as in the state-of-the-art techniques. The effectiveness of the test is evaluated by comparing it against the state-of-the-art.

Priority scheduling is a fundamental tool to provide service isolation for different jobs in shared clusters. Ideally, the performance of a high-priority job should not be dragged down by another with a lower priority. However, we show in this paper that simply assigning a high priority provides no isolation for jobs with dependent computations. A job, even receiving the highest priority, may give up compute slots to another before proceeding to the downstream computation, which is because of barrier, i.e., that the downstream computation cannot start until all the upstream tasks have completed. Such an interruption of execution inevitably results in a significant delay. In this paper, we propose speculative slot reservation that judiciously reserves slots for downstream computations, so as to retain service isolation for high-priority jobs. To mitigate the utilization loss due to slot reservation, we analyze the trade-off between utilization and isolation, and expose a tunable knob to navigate the trade-off. We also propose a complementary straggler mitigation strategy that uses the reserved slots to run extra copies of slow tasks. We have implemented speculative slot reservation in Spark. Evaluations based on both cluster deployment and trace-driven simulations show that our approach enforces strict service isolation for high-priority jobs, without slowing down the other jobs with a lower priority.

WiFi has become the primary method to access the Internet. However, the WiFi-hop latency, particularly in dense-WiFi environments, is far from satisfactory [1], to support delay-sensitive applications such as Web browsing and VoIP. The WiFi latency mainly comes from two kinds of queues: the host queue and the distributed queue, which is caused by CSMA/CA mechanism when multiple nodes contend for the channel. While the host queue can be easily bypassed using priority scheduling at end-host, the distributed queue is not. Previously, IEEE 802.11e tries to provide priorities in this distributed queue by adjusting the MAC layer parameters, but it does not scale when there are increasing number of delay-sensitive flows. In this paper, we propose and design QAir, a practical solution to reduce WiFi latency of delay-sensitive flows in dense WiFi networks. QAir takes a different approach to transfer this distributed queue to host queue. Consequently, the delay-sensitive flows can bypass the entire queue and their latency can be greatly reduced. QAir works in a distributed manner with no centralized scheduler. We have implemented QAir on commodity WiFi devices. Experimental results show that, compared to the 802.11 DCF baseline, QAir can reduce the average WiFi-hop latency of delay-sensitive flows by 50–75%.

In state-of-the-art Software Transactional Memory (STM) systems, threads carry out the execution of transactions as non-interruptible tasks. Hence, a thread can react to the injection of a higher priority transactional task and take care of its processing only at the end of the currently executed transaction. In this article we pursue a paradigm shift where the execution of an in-memory transaction is carried out as a preemptable task, so that a thread can start processing a higher priority transactional task before finalizing its current transaction. We achieve this goal in an application-transparent manner, by only relying on Operating System facilities we include in our preemptive STM architecture. With our approach we are able to re-evaluate CPU assignment across transactions along a same thread every few tens of microseconds. This is mandatory for an effective priority-aware architecture given the typically finer-grain nature of in-memory transactions compared to their counterpart in database systems. We integrated our preemptive STM architecture with the TinySTM package, and released it as open source. We also provide the results of an experimental assessment of our proposal based on running a port of the TPC-C benchmark to the STM environment.

Increasing the density of access points (APs) is often required to improve the coverage and capacity of wireless local area network (WLAN). But such high density deployment may result increased interference and hence poor network performance. The channel assignment problem under this type of highly interfering environment is a challenging task. Every link may not be activated due to the limitation of number of non-overlapping channels (NOCs) and increased adjacent channel interference in case of partially overlapping channels (POCs). Existing work tries to activate as many links as possible at every slot to maximize the throughput. If we always try to activate maximum number of links in every slot, some nodes may always suffer because of their disadvantageous positions. We present a greedy algorithm which improves the average throughput of the network and minimizes the total network interference while taking care of user fairness. The proposed algorithm uses the concept of priority scheduling in which each AP is assigning some priority value to each station (STA) and we use these priority values for channel assignment and AP-STA association. We have shown that our algorithm produces better average throughput and also schedules the user traffic earlier than the algorithm based on maximizing the number of links.

In CPU scheduling various algorithms exist like FCFS (First come first serve), SJF (Shortest job first), SRTF (Shortest remaining time first), Priority Scheduling, Round Robin (RR), MLQ (Multilevel queue), MLFQ (Multilevel feedback queue) scheduling. Multilevel Feedback Queue (MLFQ) algorithm allows the switching of processes between queues depending on their burst time. The processes switch to the next queue when burst time is greater than time quantum. Each queue can define its own scheduling policy. In this paper we have implemented MLFQ technique using small burst time for the first queue thus making it analogous to RR scheduling and using SJF prior to RR from second queue onwards gives better CPU utilization. Dynamic time quantum is also used which further improves the efficiency of the scheduling. Here the dynamic time quantum of the queues is calculated based on the burst time of the processes. Time quantum of the second queue is the burst time of the (2n/3)th process (where n is the number of processes remaining after the execution in the first queue) and time quantum of the third queue is burst time of the largest remaining process. Thus 66% of the processes get executed in the second queue and remaining processes in the last queue thus preventing the problem of starvation of huge burst time processes.

Healthcare monitoring systems (HMS) are becoming emerging and revolutionary technology for ambulatory and bedridden patients. They are also expected to offer home assistance to elderly persons while providing qualitative and low cost services. As Wireless Body Area Networks (WBANs) are designed to be part of monitoring systems, a WBAN based HMS might implement Quality of service (QoS) tools to guarantee some physiological data requirements such as data loss and delay as healthcare applications are time-sensitive and loss-sensitive. This paper focuses on mitigation of delay at the coordinator of a WBAN based HMS for emergency and medical traffic flows by proposing a new scheduling strategy named PWRR (Priority-Weighted Round Robin). The PWRR as a combination of a priority scheduling and a weighted round robin utilizes the user priorities of physiological data within the WBAN to determine how to schedule and send them off the WBAN. Analytical and simulation results show that PWRR strategy offers much improvements in terms of delay of emergency and medical data flows at the coordinator compared to the FIFO (first in fist out) strategy.

Most big data jobs are network-bound, which involve large amount of data transfers among the nodes in a cluster. Optimizing the scheduling of flows can improve big data job performance. Traditional techniques are mostly flow-based scheduling, without considering the flow correlations. In this paper, we take the dependency of the flows into account and propose traffic forecasting and job-aware priority scheduling for big data processing. First, we forecast the network traffic for flows of the same job through run-time monitoring, and assign a unique priority for each job and tag every packet in this job. Then we schedule flows of the same priority (often the same job) in a FIFO order. We implement our proposed scheme using NS-2 simulator and show that our system can increase the network utilization and reduce the job completion time.

Cloud computing facilitates flexible on-demand provisioning of IT resources over the Internet. It has proven to be very advantageous for a wide range of industries in the emerging markets by allowing them to match the infrastructure capabilities of their already established competitors. However, Cloud computing also poses several unique challenges in terms of resource allocation and scheduling. In particular, I/O bound applications have been mostly neglected in the research efforts in the area. Simulation tools play a key role in the evaluation of new resource management policies by providing an affordable and replicable testing environment. In this paper, we propose a novel simulation framework PriDynSim for priority based I/O policies, which manage the available resources to ensure adequate performance in the presence of deadline constraints. In a case study, we demonstrate how PriDynSim can be used to evaluate a resource management policy, which guarantees Quality of Service (QoS) for I/O bound applications running in a typical Cloud environment.

The rapidly increasing demand of M2M (Machine to Machine) communications poses great challenges to the capacity of cellular networks. This paper proposes a new M2M scheduling algorithm, namely, Class Based Overall Priority (CBOP) scheduling, which is designed particularly to improve uplink scheduling for a massive number of MTCDs (Machine Type Communication Devices) in LTE networks. We compare the proposed algorithm with several existing scheduling algorithms via simulations and discuss its advantages and limitations.

Femtocell technology is widely known for its ability to improve cellular services while reducing the capital and operation expenditure of cellular providers. However, this can lead to more interference and accessibility issues. There are three types of femtocell access schemes currently, which are open, closed and hybrid access. While open access improves the overall network performance, closed access is a much more preferred by the end-user since it guarantees access to the femtocell owner. For this reason, hybrid access is a compromise which releases the femtocell access to the visitor when there is surplus resource left by the owner. This paper proposes a comprehensive method to solve the femtocell access issue by designing a joint multilevel priority scheduling and femtocell access policy based on a Matrix of Conflict RRM. It will be shown through computer simulation that the proposed scheduling and access policy provide network flexibility, for the femtocell owner and cellular provider, which can be utilized to achieve more benefits for both.

Traditionally real-time systems and security have been considered as separate domains. Recent attacks on various systems with real-time properties have shown the need for a redesign of such systems to include security as a first class principle. In this paper, we propose a general model for capturing security constraints between tasks in a real-time system. This model is then used in conjunction with real-time scheduling algorithms to prevent the leakage of information via storage channels on implicitly shared resources. We expand upon a mechanism to enforce these constraints viz., cleaning up of shared resource state, and provide schedulability conditions based on fixed priority scheduling with both preemptive and non-preemptive tasks. We perform extensive evaluations, both theoretical and experimental, the latter on a hardware-in-the-loop simulator of an unmanned aerial vehicle (UAV) that executes on a demonstration platform.

Cloud computing is a distributed system that provides on demand services that are realizes with minimal efforts. Scheduling is one of the important issue in cloud computing. Scheduling is a set of polices to control the order of work performed by the computer system. The main drawback of First come first serve is that its response time and turnaround time is slow. The complexity of algorithm increased by reallocation of virtual machines. Time and energy are two important issues while selecting a scheduling strategy. Proposed new hybrid virtual machine scheduling is one of the concepts which improve the performance by using virtual machines and priority based scheduling algorithm. It also includes the mapping and priority of the jobs. This scheduling method takes the advantages of generalized priority scheduling, FCFS and Round Robin. It reduces the execution time as well as the energy. This scheduling algorithm make group of the jobs according to the energy consumption of the jobs as a high priority and low priority jobs. The proposed work will be compared with generalized priority algorithm to show the improved performance in terms of time and energy.