1) Suppose a disk has 201 cylinders, numbered from 0 to 200. At some time the disk arm is at cylinder 100, and there is a queue of disk access requests for cylinders 30, 85, 90, 100, 105, 110, 135 and 145. If Shortest-Seek Time First (SSTF) is being used for scheduling the disk access, the request for cylinder 90 is serviced after servicing ____________ number of requests. (GATE CS 2014 (A) 1 (B) 2 (C) 3 (D) 4 See this for solution.
2) Consider an operating system capable of loading and executing a single sequential user process at a time. The disk head scheduling algorithm used is First Come First Served (FCFS). If FCFS is replaced by Shortest Seek Time First (SSTF), claimed by the vendor to give 50% better benchmark results, what is the expected improvement in the I/O performance of user programs? (GATE CS 2004) (A) 50% (B) 40% (C) 25% (D) 0% See this for solution.
scheduling theory algorithms and systems solution 15
Different CPU Scheduling algorithms have different structures and the choice of a particular algorithm depends on a variety of factors. Many conditions have been raised to compare CPU scheduling algorithms.
FCFS considered to be the simplest of all operating system scheduling algorithms. First come first serve scheduling algorithm states that the process that requests the CPU first is allocated the CPU first and is implemented by using FIFO queue.
Highest Response Ratio Next is a non-preemptive CPU Scheduling algorithm and it is considered as one of the most optimal scheduling algorithms. The name itself states that we need to find the response ratio of all available processes and select the one with the highest Response Ratio. A process once selected will run till completion.
The course is application-oriented and is focused on three optimizationcontexts: production planning, service timetabling, and transportationrouting. In the first context, application examples are projectscheduling, job shop scheduling and scheduling in flexible assemblysystems. In the second context, application examples are crew andworkforce scheduling, education timetabling and employee timetabling. Inthe third context, application examples are the vehicle routingproblems, with constraints that derives from capacities, time windows,pickup and delivery or back-haul. For each case, the problem will beformulated, modeled and solved. Cases under uncertainty of data willalso be considered.The solution techniques are mainly heuristics, such as local searchmethods and metaheuristics, but exact methods, such as networksalgorithms, integer programming and branch and bound, will also beoutlined when they are feasible for the given problem. The configurationand tuning of these solvers on the specific application will be solvedby means of a systematic methology.
After the course, the student is expected to be able to: - Describe in a suitable language the basic principles of general solution methods presented in the course;- Classify problems arising in scheduling, timetabling and routing also making use of opportune formal notation. Recognize new similar problems within these schemes;- For a specific problem, discuss in detail a few among the dedicated algorithms (exact or heuristic, see syllabus);- Formulate integer programming models for the cases treated in the lecture;- Apply the general methods to new combinatorial problems that resemble in nature the ones saw in the course. Describe in a precise and written language the resulting algorithms;- Implement the designed algorithms in a specific language;- Undertake an empirical examination of the performance of the algorithms implemented and discuss the results.
Ibrahim Al-Harkan, is an assistant professor of industrial engineering at King Saud University, Saudi Arabia. He holds a B.S. in Industrial Engineering from King Saud University, M.S. and Ph.D. in Industrial Engineering from The University of Oklahoma, Norman, Oklahoma, USA. His specialized areas are production planning and control, modeling and simulation of industrial and service systems, and applied operations research. His areas of interest include production sequencing, scheduling and lot sizing; expert system; simulated annealing algorithms; genetic algorithms; tabu search; and scatter search algorithms. Dr. Al-Harkan, is a member of Saudi Industrial Engineering Society (SIES), Institute of Industrial Engineers (IIE), Institute for Operations Research and the Management Sciences (INFORMS), National Society of professional engineers (NSPE), and Industrial Engineering Honor Society (Alpha Pi Mu). Dr. Al-Harkan, is the chairman of the Saudi Industrial Engineering Society (SIES) at the Saudi Council of Engineers.
CENG 15*. Engineering Computation Using Matlab (4) (Cross-listed with NANO 15) Introduction to solution of engineering problems using computational methods. Formulating problem statements, selecting algorithms, writing computer programs, and analyzing output using MATLAB. Computational problems from nanoengineering, chemical engineering, and materials science are introduced. The course requires no prior programming skills. Students may only receive credit for one of the following: CENG 15, CENG 15R, NANO 15, NANO 15R, or MAE 8.
CENG 15R*. Engineering Computation Using Matlab Online (4) (Cross-listed with NANO 15R.) Introduction to solution of engineering problems using computational methods. Formulating problem statements, selecting algorithms, writing computer programs, and analyzing output using Matlab. Computational problems from NanoEngineering, chemical engineering, and materials science are introduced. This is a fully online, self-paced course that utilizes multi-platform instructional techniques (video, text, and instructional coding environments). The course requires no prior programming skills. Students may only receive credit for one of the following: CENG 15, CENG 15R, NANO 15, NANO 15R, or MAE 8.
@articleBerkoune2006,abstract = This paper proposes various lower bounds to the makespan of the flexible job shop scheduling problem (FJSP). The FJSP is known in the literature as one of the most difficult combinatorial optimisation problems (NP-hard). We will use genetic algorithms for the optimisation of this type of problems. The list of the demands is divided in two sets: the actual demand, which is considered as certain (a list of jobs with known characteristics), and the predicted demand, which is a list of uncertain jobs. The actual demand is scheduled in priority by the genetic algorithm. Then, the predicted demand is inserted using various methods in order to generate different scheduling solutions. Two lower bounds are given for the makespan before and after the insertion of the predicted demand. The performance of solutions is evaluated by comparing the real values obtained on many static and dynamic scheduling examples with the corresponding lower bounds.,author = Berkoune, Djamel, Mesghouni, Khaled, Rabenasolo, Besoa,journal = International Journal of Applied Mathematics and Computer Science,keywords = insertion; lower bounds; predicted demands; flexible job shop scheduling; makespan,language = eng,number = 2,pages = 263-269,title = Lower bounds for the scheduling problem with uncertain demands,url = ,volume = 16,year = 2006,
TY - JOURAU - Berkoune, DjamelAU - Mesghouni, KhaledAU - Rabenasolo, BesoaTI - Lower bounds for the scheduling problem with uncertain demandsJO - International Journal of Applied Mathematics and Computer SciencePY - 2006VL - 16IS - 2SP - 263EP - 269AB - This paper proposes various lower bounds to the makespan of the flexible job shop scheduling problem (FJSP). The FJSP is known in the literature as one of the most difficult combinatorial optimisation problems (NP-hard). We will use genetic algorithms for the optimisation of this type of problems. The list of the demands is divided in two sets: the actual demand, which is considered as certain (a list of jobs with known characteristics), and the predicted demand, which is a list of uncertain jobs. The actual demand is scheduled in priority by the genetic algorithm. Then, the predicted demand is inserted using various methods in order to generate different scheduling solutions. Two lower bounds are given for the makespan before and after the insertion of the predicted demand. The performance of solutions is evaluated by comparing the real values obtained on many static and dynamic scheduling examples with the corresponding lower bounds.LA - engKW - insertion; lower bounds; predicted demands; flexible job shop scheduling; makespanUR - ER -
The Center for Development of Emerging Storage Systems (CoDESS) has a dual mission: to push the frontiers of modern data storage systems through an integrated research program and to create a highly-trained workforce of graduate students. Current research thrusts include information and coding theory for ultra-reliable data storage systems, data reduction algorithms and communication methods for cloud storage, enabling technologies for future recording paradigms and storage devices, and resource-efficient signal processing techniques and architecture optimization.
Realization theory for rational systems deals with the problem of the existence of rational systems whose input-output behavior is given by a considered input-output or response map. The realization problem concerns specifying the conditions for the existence of rational realizations which are controllable, observable, and/or minimal. We provide necessary and sufficient conditions for the existence of such rational realizations. The algorithms for constructing rational realizations follow from the proofs of the corresponding theorems. Further, we relate minimality, controllability, and observability of rational realizations, and provide the characterization of canonical and minimal realizations up to a birational equivalence. 2ff7e9595c
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