Earliest Deadline First Scheduling Algorithm Research Articles

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardware-based scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen’s Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory’s energy consumption based on the implementation.

Enhanced uplink scheduling algorithm for efficient resource management in IEEE 802.16

Abstract Worldwide interoperability for Microwave Access (WiMAX) is based on the IEEE 802.16 standard. This technology provides broadband wireless last-mile access in a Metropolitan Area Network (MAN). The IEEE 802.16-2004 standard (i.e. Fixed WiMAX) provides specification for the Medium Access Control (MAC) and Physical (PHY) layers for WiMAX. A critical part of the MAC layer specification is scheduling, which resolves contention for bandwidth and determines the transmission order of users. The algorithms are researched under different mixes of traffic and for various characteristics of the IEEE 802.16 MAC layer. In this research the focus is on the WiMAX uplink traffic scheduling. A scheduling algorithm’s task in a multi-class network is also to categorize the users into one of the pre-defined classes. In this research, the algorithms are tailored at enhancing the collective performance of hybrid algorithms in the WiMAX domains which complement the assigned priorities. The spectrum of constraints which have been extracted from the hybrid Earliest Deadline First(EDF) + Weighted Fair Queuing (WFQ) + First in First out (FIFO) EDF+WFQ+FIFO algorithm include the static nature by which priorities are assigned and maintained during the entire duration of a transmission time and the core attributes of absolute dependence on deadline. The second constraint is embedded within the EDF scheduling algorithm and the perseverance of pursuing deadline associated weightages. In this research, the reengineering of the scheduling mechanics governing the EDF algorithms has been pursued. The dominance of the pre-stipulated deadline of the EDF algorithm is indeed acknowledged in the proposed and developed enhanced algorithm. The simulation results indicate that the legacy algorithms are not suitable for the multi-class traffic systems of WiMAX. This is because these algorithms do not explicitly incorporate the WiMAX QoS parameters into their mechanisms and are highly static. Extensive discrete – event simulation experiments have been done for the purpose of performance analysis. The performance metrics used are average throughput, average delay, missed deadline ratio and average queue size utilization ratio. The acquired results have proven that the proposed algorithms have successfully enhanced the static constrained algorithm.

The Performance Improvement of an Enhanced CPU Scheduler Using Improved D_EDF Scheduling Algorithm

With the development of cloud computing, mobile device and industry automation, virtualization plays an important role today. The core of virtualization is hypervisor which directly determines the performance of platform. Therefore, how to allocate resource effectively becomes an important issue. Xen, one of widely used open source projects, is a virtual machine monitor. Simple EDF (Earliest Deadline First) scheduler that is a dynamicpriority real-time scheduler in Xen implements the famous EDF scheduling algorithm. Due to EDF scheduling strategy has miss the deadline and inefficiency in overloaded condition, we have improved the D_EDF scheduling algorithm that combines Deadline-Monotonic scheduling strategy with EDF scheduling in virtualized environment. And we have extended the Simple EDF scheduler which provided by Xen using improved D_EDF scheduling algorithm. Our experiment demonstrates that the proposed scheduling algorithm increased the performance under CPU-intensive and memory-intensive workload in overloaded condition.

Aggregation of EDF and ACO for Enhancing Real Time System Performance

Time constraint is the main factor in real time operating system and it affects the deadline of the process. To achieve deadline, proper scheduling algorithm is required to schedule the task. In this paper an Adaptive scheduling algorithm is developed which is the combination of Earliest Deadline First (EDF) and Ant Colony Optimization (ACO). The EDF algorithm places the process in a priority queue and executed using the deadline. The priority of the processes depends upon the deadline and handles the under loaded condition. The limitation of EDF algorithm is that it cannot handle the overloaded condition. The execution of ACO algorithm is based on the execution time. Process which contains the minimum execution time is executed first. The limitation of ACO algorithm is, it takes more time for execution than EDF. Therefore, to remove the limitation of both the algorithms an Adaptive scheduling algorithm is developed. It increases performance of the system and decreases the system failure. Also the percentage of missing deadline is reduced. The advantage of an Adaptive scheduling algorithm is, it handles over-loaded and under-loaded condition simultaneously. The performance of an Adaptive scheduling algorithm is calculated in terms of Success Ratio that is the number of process scheduled and CPU Utilization. The result of execution time is compared with the EDF and ACO scheduling algorithm. The goal of an Adaptive scheduling algorithm is to show the switching between the scheduling algorithms and to decrease the system failure and increase the system performance.