The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Klaus I. Pedersen - One of the best experts on this subject based on the ideXlab platform.
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capacity optimization of spatial Preemptive Scheduling for joint urllc embb traffic in 5g new radio
Global Communications Conference, 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:Ultra-reliable and low-latency communication (URLLC) is envisioned as a primary service class of the fifth generation mobile networks. URLLC applications demand stringent radio latency requirements of 1 millisecond with 99.999% confidence. Obviously, the coexistence of the URLLC services and enhanced mobile broadband (eMBB) applications on the same spectrum imposes a challenging Scheduling problem. In this paper, we propose an enhanced spatial Preemptive Scheduling framework for URLLC-eMBB traffic coexistence. The proposed scheduler ensures an instant and interference-free signal subspace for critical URLLC transmissions, while achieving best-effort eMBB performance. Furthermore, the impacted eMBB capacity is then recovered by limited network-assisted signaling. The performance of the proposed scheduler is evaluateeed by highly detailed system level simulations of the major performance indicators. Compared to the state-of-the-art multi-traffic schedulers from industry and academia, the proposed scheduler meets the stringent URLLC latency requirements, while significantly improving the achievable ergodic capacity.
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GLOBECOM Workshops - Capacity Optimization of Spatial Preemptive Scheduling for Joint URLLC-eMBB Traffic in 5G New Radio
2018 IEEE Globecom Workshops (GC Wkshps), 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:Ultra-reliable and low-latency communication (URLLC) is envisioned as a primary service class of the fifth generation mobile networks. URLLC applications demand stringent radio latency requirements of 1 millisecond with 99.999% confidence. Obviously, the coexistence of the URLLC services and enhanced mobile broadband (eMBB) applications on the same spectrum imposes a challenging Scheduling problem. In this paper, we propose an enhanced spatial Preemptive Scheduling framework for URLLC-eMBB traffic coexistence. The proposed scheduler ensures an instant and interference-free signal subspace for critical URLLC transmissions, while achieving best-effort eMBB performance. Furthermore, the impacted eMBB capacity is then recovered by limited network-assisted signaling. The performance of the proposed scheduler is evaluateeed by highly detailed system level simulations of the major performance indicators. Compared to the state-of-the-art multi-traffic schedulers from industry and academia, the proposed scheduler meets the stringent URLLC latency requirements, while significantly improving the achievable ergodic capacity.
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null space based Preemptive Scheduling for joint urllc and embb traffic in 5g networks
Global Communications Conference, 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:In this paper, we propose a null-space-based Preemptive Scheduling framework for cross-objective optimization to always guarantee robust URLLC performance, while extracting the maximum possible eMBB capacity. The proposed scheduler perpetually grants incoming URLLC traffic a higher priority for instant Scheduling. In case that radio resources are not immediately schedulable, proposed scheduler forcibly enforces an artificial spatial user separation, for the URLLC traffic to get instantly scheduled over shared resources with ongoing eMBB transmissions. A pre-defined reference spatial subspace is constructed for which scheduler instantly picks the active eMBB user whose precoder is the closest possible. Then, it projects the eMBB precoder on-the-go onto the reference subspace, in order for its paired URLLC user to orient its decoder matrix into one possible null space of the reference subspace. Hence, a robust decoding ability is always preserved at the URLLC user, while cross-maximizing the ergodic capacity. Compared to the state-of-the-art proposals from industry and academia, proposed scheduler shows extreme URLLC latency robustness with significantly improved overall spectral efficiency. Analytical analysis and extensive system level simulations are presented to support paper conclusions.
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GLOBECOM Workshops - Null Space Based Preemptive Scheduling for Joint URLLC and eMBB Traffic in 5G Networks
2018 IEEE Globecom Workshops (GC Wkshps), 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:In this paper, we propose a null-space-based Preemptive Scheduling framework for cross-objective optimization to always guarantee robust URLLC performance, while extracting the maximum possible eMBB capacity. The proposed scheduler perpetually grants incoming URLLC traffic a higher priority for instant Scheduling. In case that radio resources are not immediately schedulable, proposed scheduler forcibly enforces an artificial spatial user separation, for the URLLC traffic to get instantly scheduled over shared resources with ongoing eMBB transmissions. A pre-defined reference spatial subspace is constructed for which scheduler instantly picks the active eMBB user whose precoder is the closest possible. Then, it projects the eMBB precoder on-the-go onto the reference subspace, in order for its paired URLLC user to orient its decoder matrix into one possible null space of the reference subspace. Hence, a robust decoding ability is always preserved at the URLLC user, while cross-maximizing the ergodic capacity. Compared to the state-of-the-art proposals from industry and academia, proposed scheduler shows extreme URLLC latency robustness with significantly improved overall spectral efficiency. Analytical analysis and extensive system level simulations are presented to support paper conclusions.
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Multi-User Preemptive Scheduling For Critical Low Latency Communications in 5G Networks
2018 IEEE Symposium on Computers and Communications (ISCC), 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:5G new radio is envisioned to support three major service classes: enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communications. Emerging URLLC services require up to one millisecond of communication latency with 99.999% success probability. Though, there is a fundamental trade-off between system spectral efficiency (SE) and achievable latency. This calls for novel Scheduling protocols which cross-optimize system performance on user-centric; instead of network-centric basis. In this paper, we develop a joint multi-user Preemptive Scheduling strategy to simultaneously cross-optimize system SE and URLLC latency. At each Scheduling opportunity, available URLLC traffic is always given higher priority. When sporadic URLLC traffic appears during a transmission time interval (TTI), proposed scheduler seeks for fitting the URLLC-eMBB traffic in a multi-user transmission. If the available spatial degrees of freedom are limited within a TTI, the URLLC traffic instantly overwrites part of the ongoing eMBB transmissions to satisfy the URLLC latency requirements, at the expense of minimal eMBB throughput loss. Extensive dynamic system level simulations show that proposed scheduler provides significant performance gain in terms of eMBB SE and URLLC latency.
Leah Epstein - One of the best experts on this subject based on the ideXlab platform.
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Preemptive Scheduling on uniformly related machines: minimizing the sum of the largest pair of job completion times
Journal of Scheduling, 2017Co-Authors: Leah Epstein, Ido YatsivAbstract:We revisit the classic problem of Preemptive Scheduling on m uniformly related machines. In this problem, jobs can be arbitrarily split into parts, under the constraint that every job is processed completely, and that the parts of a job are not assigned to run in parallel on different machines. We study a new objective which is motivated by fairness, where the goal is to minimize the sum of the two maximal job completion times. We design a polynomial time algorithm for computing an optimal solution. The algorithm can act on any set of machine speeds and any set of input jobs. The algorithm has several cases, many of which are very different from algorithms for makespan minimization (algorithms that minimize the maximum completion time of any job), and from algorithms that minimize the total completion time of all jobs.
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Robust Algorithms for Preemptive Scheduling
Algorithmica, 2014Co-Authors: Leah Epstein, Asaf LevinAbstract:Preemptive Scheduling problems on parallel machines are classic problems. Given the goal of minimizing the makespan, they are polynomially solvable even for the most general model of unrelated machines. In these problems, a set of jobs is to be assigned to run on a set of m machines. A job can be split into parts arbitrarily and these parts are to be assigned to time slots on the machines without parallelism, that is, for every job, at most one of its parts can be processed at each time. Motivated by sensitivity analysis and online algorithms, we investigate the problem of designing robust algorithms for constructing Preemptive schedules. Robust algorithms receive one piece of input at a time. They may change a small portion of the solution as an additional part of the input is revealed. The capacity of change is based on the size of the new piece of input. For Scheduling problems, the supremum ratio between the total size of the jobs (or parts of jobs) which may be re-scheduled upon the arrival of a new job j , and the size of j , is called migration factor . We design a strongly optimal algorithm with the migration factor $1-\frac{1}{m}$ for identical machines. Strongly optimal algorithms avoid idle time and create solutions where the (non-increasingly) sorted vector of completion times of the machines is lexicographically minimal. In the case of identical machines this results not only in makespan minimization, but the created solution is also optimal with respect to any ℓ _ p norm (for p >1). We show that an algorithm of a smaller migration factor cannot be optimal with respect to makespan or any other ℓ _ p norm, thus the result is best possible in this sense as well. We further show that neither uniformly related machines nor identical machines with restricted assignment admit an optimal algorithm with a constant migration factor. This lower bound holds both for makespan minimization and for any ℓ _ p norm. Finally, we analyze the case of two machines and show that in this case it is still possible to maintain an optimal schedule with a small migration factor in the cases of two uniformly related machines and two identical machines with restricted assignment.
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robust algorithms for Preemptive Scheduling
European Symposium on Algorithms, 2011Co-Authors: Leah Epstein, Asaf LevinAbstract:Preemptive Scheduling problems on parallel machines are classic problems. Given the goal of minimizing the makespan, they are polynomially solvable even for the most general model of unrelated machines. In these problems, a set of jobs is to be assigned to be executed on a set of m machines. A job can be split into parts arbitrarily and these parts are to be assigned to time slots on the machines without parallelism, that is, for every job, at most one of its parts can be processed at each time. Motivated by sensitivity analysis and online algorithms, we investigate the problem of designing robust algorithms for constructing Preemptive schedules. Robust algorithms receive one piece of input at a time. They may change a small portion of the solution as an additional part of the input is revealed. The capacity of change is based on the size of the new input. For Scheduling problems, the maximum ratio between the total size of the jobs (or parts of jobs) which may be re-scheduled upon the arrival of a new job j, and the size of j, is called migration factor. We design a strongly optimal algorithm with the migration factor 1- 1/m for identical machines. Such algorithms avoid idle time and create solutions where the (non-increasingly) sorted vector of completion times of the machines is minimal lexicographically. In the case of identical machines this results not only in makespan minimization, but the created solution is also optimal with respect to any lp norm (for p > 1). We show that an algorithm of a smaller migration factor cannot be optimal with respect to makespan or any other norm, thus the result is best possible in this sense as well. We further show that neither uniformly related machines nor identical machines with restricted assignment admit an optimal algorithm with a constant migration factor. This lower bound holds both for makespan minimization and for any lp norm. Finally, we analyze the case of two machines and show that in this case it is still possible to maintain an optimal schedule with a small migration factor in the cases of two uniformly related machines and two identical machines with restricted assignment.
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ESA - Robust algorithms for Preemptive Scheduling
Algorithms – ESA 2011, 2011Co-Authors: Leah Epstein, Asaf LevinAbstract:Preemptive Scheduling problems on parallel machines are classic problems. Given the goal of minimizing the makespan, they are polynomially solvable even for the most general model of unrelated machines. In these problems, a set of jobs is to be assigned to be executed on a set of m machines. A job can be split into parts arbitrarily and these parts are to be assigned to time slots on the machines without parallelism, that is, for every job, at most one of its parts can be processed at each time. Motivated by sensitivity analysis and online algorithms, we investigate the problem of designing robust algorithms for constructing Preemptive schedules. Robust algorithms receive one piece of input at a time. They may change a small portion of the solution as an additional part of the input is revealed. The capacity of change is based on the size of the new input. For Scheduling problems, the maximum ratio between the total size of the jobs (or parts of jobs) which may be re-scheduled upon the arrival of a new job j, and the size of j, is called migration factor. We design a strongly optimal algorithm with the migration factor 1- 1/m for identical machines. Such algorithms avoid idle time and create solutions where the (non-increasingly) sorted vector of completion times of the machines is minimal lexicographically. In the case of identical machines this results not only in makespan minimization, but the created solution is also optimal with respect to any lp norm (for p > 1). We show that an algorithm of a smaller migration factor cannot be optimal with respect to makespan or any other norm, thus the result is best possible in this sense as well. We further show that neither uniformly related machines nor identical machines with restricted assignment admit an optimal algorithm with a constant migration factor. This lower bound holds both for makespan minimization and for any lp norm. Finally, we analyze the case of two machines and show that in this case it is still possible to maintain an optimal schedule with a small migration factor in the cases of two uniformly related machines and two identical machines with restricted assignment.
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MFCS - Optimal Preemptive Scheduling for General Target Functions
Lecture Notes in Computer Science, 2004Co-Authors: Leah Epstein, Tamir TassaAbstract:We study the problem of optimal Preemptive Scheduling with respect to a general target function. Given n jobs with associated weights and m ≤ n uniformly related machines, one aims at Scheduling the jobs to the machines, allowing preemptions but forbidding parallelization, so that a given target function of the loads on each machine is minimized. This problem was studied in the past in the case of the makespan. Gonzalez and Sahni [7] and later Shachnai, Tamir and Woeginger [12] devised a polynomial algorithm that outputs an optimal schedule for which the number of preemptions is at most 2(m–1). We extend their ideas for general symmetric, convex and monotone target functions. This general approach enables us to distill the underlying principles on which the optimal makespan algorithm is based. More specifically, the general approach enables us to identify between the optimal Scheduling problem and a corresponding problem of mathematical programming. This, in turn, allows us to devise a single algorithm that is suitable for a wide array of target functions, where the only difference between one target function and another is manifested through the corresponding mathematical programming problem.
Ali A. Esswie - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM Workshops - Capacity Optimization of Spatial Preemptive Scheduling for Joint URLLC-eMBB Traffic in 5G New Radio
2018 IEEE Globecom Workshops (GC Wkshps), 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:Ultra-reliable and low-latency communication (URLLC) is envisioned as a primary service class of the fifth generation mobile networks. URLLC applications demand stringent radio latency requirements of 1 millisecond with 99.999% confidence. Obviously, the coexistence of the URLLC services and enhanced mobile broadband (eMBB) applications on the same spectrum imposes a challenging Scheduling problem. In this paper, we propose an enhanced spatial Preemptive Scheduling framework for URLLC-eMBB traffic coexistence. The proposed scheduler ensures an instant and interference-free signal subspace for critical URLLC transmissions, while achieving best-effort eMBB performance. Furthermore, the impacted eMBB capacity is then recovered by limited network-assisted signaling. The performance of the proposed scheduler is evaluateeed by highly detailed system level simulations of the major performance indicators. Compared to the state-of-the-art multi-traffic schedulers from industry and academia, the proposed scheduler meets the stringent URLLC latency requirements, while significantly improving the achievable ergodic capacity.
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capacity optimization of spatial Preemptive Scheduling for joint urllc embb traffic in 5g new radio
Global Communications Conference, 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:Ultra-reliable and low-latency communication (URLLC) is envisioned as a primary service class of the fifth generation mobile networks. URLLC applications demand stringent radio latency requirements of 1 millisecond with 99.999% confidence. Obviously, the coexistence of the URLLC services and enhanced mobile broadband (eMBB) applications on the same spectrum imposes a challenging Scheduling problem. In this paper, we propose an enhanced spatial Preemptive Scheduling framework for URLLC-eMBB traffic coexistence. The proposed scheduler ensures an instant and interference-free signal subspace for critical URLLC transmissions, while achieving best-effort eMBB performance. Furthermore, the impacted eMBB capacity is then recovered by limited network-assisted signaling. The performance of the proposed scheduler is evaluateeed by highly detailed system level simulations of the major performance indicators. Compared to the state-of-the-art multi-traffic schedulers from industry and academia, the proposed scheduler meets the stringent URLLC latency requirements, while significantly improving the achievable ergodic capacity.
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null space based Preemptive Scheduling for joint urllc and embb traffic in 5g networks
Global Communications Conference, 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:In this paper, we propose a null-space-based Preemptive Scheduling framework for cross-objective optimization to always guarantee robust URLLC performance, while extracting the maximum possible eMBB capacity. The proposed scheduler perpetually grants incoming URLLC traffic a higher priority for instant Scheduling. In case that radio resources are not immediately schedulable, proposed scheduler forcibly enforces an artificial spatial user separation, for the URLLC traffic to get instantly scheduled over shared resources with ongoing eMBB transmissions. A pre-defined reference spatial subspace is constructed for which scheduler instantly picks the active eMBB user whose precoder is the closest possible. Then, it projects the eMBB precoder on-the-go onto the reference subspace, in order for its paired URLLC user to orient its decoder matrix into one possible null space of the reference subspace. Hence, a robust decoding ability is always preserved at the URLLC user, while cross-maximizing the ergodic capacity. Compared to the state-of-the-art proposals from industry and academia, proposed scheduler shows extreme URLLC latency robustness with significantly improved overall spectral efficiency. Analytical analysis and extensive system level simulations are presented to support paper conclusions.
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GLOBECOM Workshops - Null Space Based Preemptive Scheduling for Joint URLLC and eMBB Traffic in 5G Networks
2018 IEEE Globecom Workshops (GC Wkshps), 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:In this paper, we propose a null-space-based Preemptive Scheduling framework for cross-objective optimization to always guarantee robust URLLC performance, while extracting the maximum possible eMBB capacity. The proposed scheduler perpetually grants incoming URLLC traffic a higher priority for instant Scheduling. In case that radio resources are not immediately schedulable, proposed scheduler forcibly enforces an artificial spatial user separation, for the URLLC traffic to get instantly scheduled over shared resources with ongoing eMBB transmissions. A pre-defined reference spatial subspace is constructed for which scheduler instantly picks the active eMBB user whose precoder is the closest possible. Then, it projects the eMBB precoder on-the-go onto the reference subspace, in order for its paired URLLC user to orient its decoder matrix into one possible null space of the reference subspace. Hence, a robust decoding ability is always preserved at the URLLC user, while cross-maximizing the ergodic capacity. Compared to the state-of-the-art proposals from industry and academia, proposed scheduler shows extreme URLLC latency robustness with significantly improved overall spectral efficiency. Analytical analysis and extensive system level simulations are presented to support paper conclusions.
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Multi-User Preemptive Scheduling For Critical Low Latency Communications in 5G Networks
2018 IEEE Symposium on Computers and Communications (ISCC), 2018Co-Authors: Ali A. Esswie, Klaus I. PedersenAbstract:5G new radio is envisioned to support three major service classes: enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communications. Emerging URLLC services require up to one millisecond of communication latency with 99.999% success probability. Though, there is a fundamental trade-off between system spectral efficiency (SE) and achievable latency. This calls for novel Scheduling protocols which cross-optimize system performance on user-centric; instead of network-centric basis. In this paper, we develop a joint multi-user Preemptive Scheduling strategy to simultaneously cross-optimize system SE and URLLC latency. At each Scheduling opportunity, available URLLC traffic is always given higher priority. When sporadic URLLC traffic appears during a transmission time interval (TTI), proposed scheduler seeks for fitting the URLLC-eMBB traffic in a multi-user transmission. If the available spatial degrees of freedom are limited within a TTI, the URLLC traffic instantly overwrites part of the ongoing eMBB transmissions to satisfy the URLLC latency requirements, at the expense of minimal eMBB throughput loss. Extensive dynamic system level simulations show that proposed scheduler provides significant performance gain in terms of eMBB SE and URLLC latency.
Jiri Sgall - One of the best experts on this subject based on the ideXlab platform.
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STACS - Semi-Online Preemptive Scheduling: One Algorithm for All Variants.
2009Co-Authors: Tomáš Ebenlendr, Jiri SgallAbstract:We present a unified optimal semi-online algorithm for Preemptive Scheduling on uniformly related machines with the objective to minimize the makespan. This algorithm works for all types of semi-online restrictions, including the ones studied before, like sorted (decreasing) jobs, known sum of processing times, known maximal processing time, their combinations, and so on. Based on the analysis of this algorithm, we derive some global relations between various semi-online restrictions and tight bounds on the approximation ratios for a small number of machines.
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Optimal and online Preemptive Scheduling on uniformly related machines
Lecture Notes in Computer Science, 2004Co-Authors: Tomáš Ebenlendr, Jiri SgallAbstract:We consider the problem of Preemptive Scheduling on uniformly related machines. We present a semi-online algorithm which, if the optimal makespan is given in advance, produces an optimal schedule. Using the standard doubling technique, this yields a 4 competitive deterministic and e ≃ 2.71 competitive randomized online algorithms. In addition, it matches the performance of the previously known algorithms for the offline case, with a considerably simpler proof. Finally, we study the performance of greedy heuristics for the same problem.
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STACS - Optimal and Online Preemptive Scheduling on Uniformly Related Machines
Lecture Notes in Computer Science, 2004Co-Authors: Tomáš Ebenlendr, Jiri SgallAbstract:We consider the problem of Preemptive Scheduling on uniformly related machines. We present a semi-online algorithm which, if the optimal makespan is given in advance, produces an optimal schedule. Using the standard doubling technique, this yields a 4 competitive deterministic and e≈ 2.71 competitive randomized online algorithms. In addition, it matches the performance of the previously known algorithms for the offline case, with a considerably simpler proof. Finally, we study the performance of greedy heuristics for the same problem.
Tamir Tassa - One of the best experts on this subject based on the ideXlab platform.
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MFCS - Optimal Preemptive Scheduling for General Target Functions
Lecture Notes in Computer Science, 2004Co-Authors: Leah Epstein, Tamir TassaAbstract:We study the problem of optimal Preemptive Scheduling with respect to a general target function. Given n jobs with associated weights and m ≤ n uniformly related machines, one aims at Scheduling the jobs to the machines, allowing preemptions but forbidding parallelization, so that a given target function of the loads on each machine is minimized. This problem was studied in the past in the case of the makespan. Gonzalez and Sahni [7] and later Shachnai, Tamir and Woeginger [12] devised a polynomial algorithm that outputs an optimal schedule for which the number of preemptions is at most 2(m–1). We extend their ideas for general symmetric, convex and monotone target functions. This general approach enables us to distill the underlying principles on which the optimal makespan algorithm is based. More specifically, the general approach enables us to identify between the optimal Scheduling problem and a corresponding problem of mathematical programming. This, in turn, allows us to devise a single algorithm that is suitable for a wide array of target functions, where the only difference between one target function and another is manifested through the corresponding mathematical programming problem.
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Optimal Preemptive Scheduling for general target functions
Lecture Notes in Computer Science, 2004Co-Authors: Leah Epstein, Tamir TassaAbstract:We study the problem of optimal Preemptive Scheduling with respect to a general target function. Given n jobs with associated weights and m < n uniformly related machines, one aims at Scheduling the jobs to the machines, allowing preemptions but forbidding parallelization, so that a given target function of the loads on each machine is minimized. This problem was studied in the past in the case of the makespan. Gonzalez and Sahni [7] and later Shachnai, Tamir and Woeginger [12] devised a polynomial algorithm that outputs an optimal schedule for which the number of preemptions is at most 2(m - 1). We extend their ideas for general symmetric, convex and monotone target functions. This general approach enables us to distill the underlying principles on which the optimal makespan algorithm is based. More specifically, the general approach enables us to identify between the optimal Scheduling problem and a corresponding problem of mathematical programming. This, in turn, allows us to devise a single algorithm that is suitable for a wide array of target functions, where the only difference between one target function and another is manifested through the corresponding mathematical programming problem.
