FIFO Order Research Articles

A New Hybrid COA-OOA Based Task Scheduling and Fuzzy Logic Approach to Increase Fault Tolerance in Cloud Computing

INTRODUCTION: Technology is made available to customers worldwide through a distributed computing architecture called cloud computing. In the cloud paradigm, there is a risk of single-point failures, in order to prevent errors and gain confidence from consumers in their cloud services, one problem facing cloud providers is efficiently scheduling tasks.OBJECTIVES: High availability and fault tolerance must be offered to clients by these services. Fuzzy logic and hybrid COA-OOA are used in this study proposed fault-tolerant work scheduling algorithm. Jobs given by users and virtual machines are considered as input for this proposed approach. METHODS: The given tasks are initially scheduled utilizing the FIFO order. Then, it is rescheduled utilizing the Hybrid Coati Optimization Algorithm (COA) - Osprey Optimization Algorithm (OOA) for scheduling the task based on priority.RESULTS: This scheduled job is assigned to the VM for further execution. If the jobs are not executed successfully, then fault tolerant mechanism is carried out. Faults are recognized by employing fuzzy logic in this proposed approach. CONCLUSION: This proposed approach attains 62 sec response time, 61 sec of makespan and 98% success rate. Thus, this proposed approach is the best choice for efficient task scheduling with fault tolerant mechanism.

Multi-criteria Optimization of Real-time DAGs on Heterogeneous Platforms under P-EDF

This article tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each directed-acyclic-graph (DAG) having a minimum inter-arrival period for its activation requests and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with Dynamic Voltage and Frequency Scaling (DVFS) capabilities, including big.LITTLE Arm architectures and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by minimizing power consumption, respecting precise timing constraints, maximizing the applications’ slack, respecting given power consumption constraints, or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs.-hardware implementation choice for tasks that can be hardware accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals.

Bi-modal macroscopic traffic dynamics in a single region

Bi-modal or 3D-MFD relates the accumulation of cars, buses to total production at the network level. The current work provides a detailed discussion of extended MFD-based models namely, accumulation-based and trip-based models that accounts for bi-modal flows through 3D-MFD. In addition, delay accumulation-based models, also known as exit-flow models in classical traffic flow theory, are revisited. Fundamental modeling differences between different MFD-based models are illustrated using a benchmark test case. A new FIFO-based entry flow function is also proposed in order to address the inconsistencies of the conventional entry flow function in the context of the 3D-MFD case. A novel weak internal FIFO discipline is proposed to circumnavigate the violation of internal FIFO order during network unloading in the delay accumulation-based model. MFD-based models are verified using the solutions of micro-simulations performed on an idealized grid network. The importance of separating the 3D-MFD into partial car and bus 3D-MFDs is highlighted. Moreover, it is also shown that partial bus 3D-MFD should be further split when dedicated bus lanes are present in the network to account for unequal mean speeds between different bus lanes.

Efficient Event Scheduling of Network Update

Changes in network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update that groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but it avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. This improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to $10 \times $ faster than the flow-level scheduling method for network update events, and its tail ECT is up to $6 \times $ faster. Our P-LMTF method incurs a 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

Consensus using omega in asynchronous systems with unknown membership and degenerative Byzantine failures

We study consensus in asynchronous systems where membership is unknown, and where up to f degenerative Byzantine failures can happen. In our failure model a faulty process can have a Byzantine behavior (i.e., it deviates from its specification), and, furthermore, every faulty process will degenerate such that eventually it will have permanent physical or transmission failures. We present a simple algorithm that solves Consensus using the Omega failure detector and a new broadcast primitive called RFLOB in an asynchronous system with degenerative Byzantine failures, which is optimal with respect to failures because it works when f<n/3. RFLOB guarantees reliable, FIFO and local order broadcast in systems with Byzantine processes and unknown membership. Finally, we present an algorithm that implements an Omega failure detector with unknown membership and minimum connectivity (i.e., communication reliability, and synchrony properties) in a system with degenerative Byzantine failures.

Analysis on the Inventory Management for Real Dairy Industries Pvt. Ltd.

Inventory Management is a vital component for an organisation which deals with the physical goods. An efficient inventory management reduces the cost of the production which affects the cost of the finished goods, a producer will attach the cost of the production to the finished goods. To keep the cost low, inventory management is a must for any organisation. A study has been conducted on the Real Dairy Industries Private Limited in order to understand the inventory management techniques used by the organisation. The organisations inventory management techniques has been incorporated with different models which are FIFO and Economic Ordering Quantity. Company's inventory list and finished goods list have also been used to understand the Inventory management.

FIFO Order reliable convergecast in WBAN

This paper is the first extensive work on FIFO order reliable convergecast in multi-hop Wireless Body Area Networks (WBAN). Convergecast is a many-to-one cooperative scheme where each node of the network transmits data towards the same sink. In critical application of WBAN such as medical, packets loss or inversion have an important impact at the application level. Our contribution is threefold. First, we stress existing WBAN convergecast strategies with respect to their capacity to be reliable and verify the FIFO order of message reception (packets sent in a specific order by a node should be received in the same order by the sink). When stressed with transmission rates up to 500 packets per second and various human body postures, the performances of all these strategies decrease dramatically (more than 90% of packets are lost). This dramatic reliability drop has never been exposed in wireless body area network. Secondly, we propose a new posture-centric link model for WBAN. This model offers a good characterization of paths availability from each node to the sink, and is further used to fine tune the retransmission rates. Third, based on our model we propose a new mechanism for reliability, Non-ACK Retransmission, and a new converge-cast tree based strategy that outperforms WBAN dedicated strategies but also strategies adapted from DTN and WSN areas. We performed extensive evaluations with seven different human body postures. We targeted the following parameters: packet loss, FIFO order reliability and energy consumption. Our new strategy ensures zero packet order inversions for various transmission rates and mobility postures. Interestingly, our strategy verifies this property without the need of additional energy-guzzler mechanisms.

A Single-Tier Virtual Queuing Memory Controller Architecture for Heterogeneous MPSoCs

Heterogeneous MPSoCs typically integrate diverse cores, including application CPUs, GPUs, and HD coders. These cores commonly share an off-chip memory to save cost and energy, but their memory accesses often interfere with each other, leading to undesirable consequences like a slowdown of application performance or a failure to sustain real-time performance. The memory controller plays a central role in meeting the QoS needs of real-time cores while maximizing CPU performance. Previous QoS-aware memory controllers are based on a classic two-tier queuing architecture that buffers memory transactions at the first tier, followed by a second tier that buffers translated DRAM commands. In these designs, QoS-aware policies are used to schedule competing transactions at the first stage, but the translated DRAM commands are served in FIFO order at the second stage. Unfortunately, once the scheduled transactions have been forwarded to the command stage, newly arriving transactions that may be more critical cannot be served ahead of those translated commands that are already queued at the second stage. To address this, we propose a scalable memory controller architecture based on single-tier virtual queuing (STVQ) that maintains a single tier of request queues and employs an efficacious scheduler that considers both QoS requirements and DRAM bank states. In comparison with previous QoS-aware memory controllers, the proposed STVQ memory controller reduces CPU slowdown by up to 13.9% while satisfying all frame rate requirements. We propose further optimizations that can significantly increase row-buffer hits by up to 66.2% and reduce memory latency by up to 19.8%.

Tidex

Several basic mutual exclusion lock algorithms are known, with one of the simplest being the Ticket Lock. We present a new mutual exclusion lock with properties similar to the Ticket Lock but using atomic_exchange() instead of atomic_fetch_add() that can be more efficient on systems without a native instruction for atomic_fetch_add(), or in which the native instruction for atomic_exchange() is faster than the one for atomic_fetch_add(). Similarly to the Ticket Lock, our lock has small memory foot print, is extremely simple, respects FIFO order, and provides starvation freedom in architectures that implement atomic_exchange() as a single instruction, like x86.

Efficient Coflow Scheduling Without Prior Knowledge

Inter-coflow scheduling improves application-level communication performance in data-parallel clusters. However, existing efficient schedulers require a priori coflow information and ignore cluster dynamics like pipelining, task failures, and speculative executions, which limit their applicability. Schedulers without prior knowledge compromise on performance to avoid head-of-line blocking. In this paper, we present Aalo that strikes a balance and efficiently schedules coflows without prior knowledge. Aalo employs Discretized Coflow-Aware Least-Attained Service (D-CLAS) to separate coflows into a small number of priority queues based on how much they have already sent across the cluster. By performing prioritization across queues and by scheduling coflows in the FIFO order within each queue, Aalo's non-clairvoyant scheduler reduces coflow completion times while guaranteeing starvation freedom. EC2 deployments and trace-driven simulations show that communication stages complete 1.93X faster on average and 3.59X faster at the 95th percentile using Aalo in comparison to per-flow mechanisms. Aalo's performance is comparable to that of solutions using prior knowledge, and Aalo outperforms them in presence of cluster dynamics.

Delay analysis of a queue with re-sequencing buffer and Markov environment

There are simple service disciplines where the system time of a tagged customer depends only on the customers arriving in the system earlier (for example first-in-first-out (FIFO)) or later (for example LIFO) than the tagged one. In this paper we consider a single-server queueing system with two infinite queues in which the system time of a tagged customer may depend on both the customers arriving in the system earlier and later than the tagged one. New regular customers arrive in the system according to Markov arrival process (MAP) flow, occupy one place in the buffer and receive service in FIFO order. External re-sequencing signals also arrive at the system according to (different) MAP flow. Each re-sequencing signal transforms one regular customer into a delayed one by moving it to another queue (re-sequencing buffer), wherefrom it is served with lower priority than the regular ones. Service times of customers from both queues also have MAP distribution different from those which govern arrivals. Queueing system with memoryless ingredients (arrival, service, resequencing) has already been a subject of extensive research. In this paper we investigate how the essential analytical properties of scalar functions, which made the analysis of the memoryless system feasible, can be extended to the case of a Markov environment.

IJARCCE - Computer and Communication Engineering

This paper discusses Cloud Computing is rising as a replacement machine paradigm shift. Hadoop- MapReduce has become a robust Computation Model for process giant knowledge on distributed commodity hardware clusters like Clouds. Studies describe that Hadoop implementations, the default first in first out scheduler hardware is accessible wherever jobs are scheduled in FIFO order with support for different priority primarily based schedulers also other defaulter schedulers . During this paper we have studied numerous scheduling enhancements in a scheduling techniques a brand new with Hadoop like Fair4s scheduling algorithm with its extended functionalities allows processing large as well small jobs with effective fairness without starvation of small jobs. Majority of small jobs are available till date so Job scheduling algorithm must take in consideration of small jobs first with effective major to process these jobs.

Hadoop Scheduler with Deadline Constraint

A popular program ming model for running data intensive applications on the cloud is map reduce. In the Hadoop usually, jobs are scheduled in FIFO order by default. There are many map reduce applications which require strict deadline. In Hadoop framework, scheduler w i t h deadline c o n s t r a i n t s has not been implemented. Existing schedulers d o not guarantee that the job will be completed by a specific deadline. Some schedulers address the issue of deadlines but focus more on improving s y s t e m utilization. We have proposed an algorithm which facilitates the user to specify a jobs deadline and evaluates whether the job can be finished before the deadline. Scheduler with deadlines for Hadoop, which ensures that only jobs, whose deadlines c an be met are scheduled f o r execution. If the job submitted does not satisfy the specified deadline, physical or virtual nodes can be added dynamically to complete the job within deadline(8).

Decentralized task-aware scheduling for data center networks

Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a user's news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation -- rather than as part of a task -- and therefore only optimize flow-level metrics. In this paper, we show that task-aware network scheduling, which groups flows of a task and schedules them together, can reduce both the average as well as tail completion time for typical data center applications. To achieve these benefits in practice, we design and implement Baraat, a decentralized task-aware scheduling system. Baraat schedules tasks in a FIFO order but avoids head-of-line blocking by dynamically changing the level of multiplexing in the network. Through experiments with Memcached on a small testbed and large-scale simulations, we show that Baraat outperforms state-of-the-art decentralized schemes (e.g., pFabric) as well as centralized schedulers (e.g., Orchestra) for a wide range of workloads (e.g., search, analytics, etc).

Space optimal solution for data reordering in streaming applications on NoC based MPSoC

Many streaming applications feature coarse grain task farm or pipeline parallelism and can be modeled as a set of parallel threads. Performance requirements can often only be met by mapping the application onto a Multi Processor System-on-Chip (MPSoC). To avoid contention, hierarchical interconnection networks, where the central interconnect is a network-on-chip, are employed. In such a clustered MPSoC, the memory access latency varies strongly depending on the location of data, and is the principal cause of out-of-order arrival of data items. We present an algorithm which re-establishes the order of data items on the output side. If their earliness or lateness exceeds a limit previously fixed by experimentation, they are dropped, otherwise stored in a buffer. Write operations to this buffer are random access, whereas read operations are in FIFO order. Our algorithm guarantees that no data is removed from the buffer before it has been read, and, for a given throughput, minimum buffer size. The algorithm was implemented within the output co-processors for three application case studies and validated on a simulation platform based on the SoCLib library.

Quality of Service and Performance Analysis with Reactive Protocol in MANET by a Fuzzy Scheduler

This paper presents an analysis of DSR protocol and packet scheduling algorithm to find those that most improve performance in congested network and proposes our algorithm to improve the performance of DSR protocol by using fuzzy logic in mobile ad hoc networks. Packet schedulers in wireless ad hoc networks serve data packets in FIFO order. Because, a scheduling algorithm to schedule the packet based on their priorities will improve the performance of the network. It is found that the scheduler provides overall improvement in the performance of the system when evaluated under different load and mobility conditions. The performance of this scheduler has been studied using NS2.34 simulator and measured such as packet delivery ratio, end-toend delay and throughput and an attempt to apply fuzzy logic in the design and implementation of a rule based scheduling algorithm to solve the shortcoming of well-known drop tail algorithms. Our main contribution is proposing a fuzzy approach to multi-packets scheduling in which the scheduling parameters are treated as fuzzy variables. It is concluded that the proposed fuzzy approach is very promising and it has the potential to be considered for future research.

A Priority Scheduler Based Qos For Dynamic Source Routing Protocol Using Fuzzy Logic In Mobile Ad-hoc Network

Mobile ad hoc network is an autonomous system of mobile nodes characterized by wireless links. A wireless Ad-hoc network consists of wireless nodes communicating without the need for a centralized administration, in which all nodes potentially contribute to the routing process. In this paper we design a fuzzy-based priority scheduler to determine the priority of the packets. We analyze packet scheduling algorithm to find those that most improve performance in congested network. Hence, a scheduling algorithm to schedule the packet based on their priorities will improve the performance of the network. Packet schedulers in wireless ad hoc networks serve data packets in FIFO order. Here, we present a fuzzy based priority scheduler for mobile ad-hoc networks, to determine the priority of the packets using Destination Sequenced Distance Vector (DSRs) as the routing protocols. The performance of this scheduler has been studied using OPNET simulator and measured such as packet delivery ratio, end-to-end delay and throughput. It is found that the scheduler provides overall improvement in the performance of the system when evaluated under different load and mobility conditions.

A discrete time bulk service queue with accessible batch: Geo/ NB (L,K) /1

This paper considers a discrete-time queue of packets awaiting movement according to the bulk service rule (L, K). Time is assumed to be divided into equal intervals called slots. The analysis has been carried out under the assumption that (i) service times of batches are independent of the number of packets in any batch, and (ii) there is no simultaneous arrival of packets and departure of batch in a single slot. The packets arrive one by one and their inter arrival times follow geometric distribution. The arriving packets are queued in FIFO order. One server transports packets in batches of minimum number L and maximum K and service times follow negative binomial NB(α, p1) distribution. The server accesses new arrivals even after service has started on any batch of initial number ‘j’ (L ≤ j < K). This operation continues till the random service time of the ongoing batch is completed or the maximum capacity of the batch being served attains “K’ whichever occurs first. The distribution of system occupancy just before and after the departure epochs is obtained using discrete-time analysis (DTA). The primary focus is on the various performance measures of the steady state distribution of the batch server at departure instants and also on numerical illustrations.

Coordinating mobile agents in interaction spaces

We present a kernel coordination language for mobile agent systems that considers as first-class citizens both the agents and the channels they use to interact with each other. Channels implement distributed, asynchronous communications with FIFO ordering and multicast routing. Features related to mobility include agent migration as well as remote cloning. Most importantly, a virtual form of channel mobility inspired by the π -calculus is also supported. This expressive feature allows mobile agents to adapt dynamically to their changing environment. The language semantics, presented formally, is based on a geometrical model named the Interaction Spaces. This provides an intuitive interpretation of the agent features and capabilities in terms of combined spatial projections and transformations. Through spatial composition, we show that standard labeled transition systems and bisimulation-based semantics may be defined above the geometry, enabling reasoning and formal verification. Finally, we describe prototype implementations of the proposed model and language.

A SIMULATION BASED APPROACH FOR OPTIMIZATION OF MULTI-STAGE PRODUCTION-DISTRIBUTION SYSTEMS WITH BATCH ORDER

This paper deals with inventory cost optimization of a multi-stage production-distribution system that is composed of a series of plants separated by warehouses for semi-finished goods and finished goods. Customer orders arrive randomly at the finished goods warehouse with random order size. Each warehouse is controlled according to a (R, nQ) inventory policy. The production orders sent by a warehouse to its upstream plant are processed in a FIFO order and shipped to the warehouse order by order. Transportation time from plant to warehouse is fixed. To minimize the average total inventory holding and demand backlogging cost, we developed a simulation based optimization method. A special attention is given to optimizing the simulation allocation budget. Numerical experiences show that our new method can efficiently solve the discrete optimization problem proposed in this paper.